Maternal Black Race and Persistent Wheezing Illness in Former Extremely Low Gestational Age Newborns: Secondary Analysis of a Randomized Trial.

ObjectiveTo evaluate the relationship between maternal self-reported race/ethnicity and persistent wheezing illness in former high-risk, extremely low gestational age newborns, and to quantify the contribution of socioeconomic, environmental, and biological factors on this relationship.Study designWe assessed persistent wheezing illness determined at 18-24 months corrected (for prematurity) age in survivors of a randomized trial. Parents/caregivers were surveyed for wheeze and inhaled asthma medication use quarterly to 12 months, and at 18 and 24 months. We used multivariable analysis to evaluate the relationship of maternal race to persistent wheezing illness, and identified mediators for this relationship via formal mediation analysis.ResultsOf 420 infants (25.2 ± 1.2 weeks of gestation and 714 ± 166 g at birth, 57% male, 34% maternal black race), 189 (45%) had persistent wheezing illness. After adjustment for gestational age, birth weight, and sex, infants of black mothers had increased odds of persistent wheeze compared with infants of nonblack mothers (OR = 2.9, 95% CI 1.9, 4.5). Only bronchopulmonary dysplasia, breast milk diet, and public insurance status were identified as mediators. In this model, the direct effect of race accounted for 69% of the relationship between maternal race and persistent wheeze, whereas breast milk diet, public insurance status, and bronchopulmonary dysplasia accounted for 8%, 12%, and 10%, respectively.ConclusionsAmong former high-risk extremely low gestational age newborns, infants of black mothers have increased odds of developing persistent wheeze. A substantial proportion of this effect is directly accounted for by race, which may reflect unmeasured environmental influences, and acquired and innate biological differences.Trial registrationClinicalTrials.gov: NCT01022580

Lessons learned from the eMERGE Network: balancing genomics in discovery and practice

The Electronic Medical Records and Genomics (eMERGE) Network, established in 2007, is a consortium of academic and integrated health systems conducting discovery and implementation research in translational genomics. Here, we outline the history of the network, highlight major impacts and lessons learned, and present the tools and resources developed for large-scale genomic analyses and translation into a clinical setting. The network developed methods to extract phenotypes from the electronic medical record to perform genome-wide and phenome-wide association studies. Recruited cohorts were clinically sequenced off a custom panel for targeted sequencing of variants and monogenic disease risks and returned to participants to investigate the impact of return of genomic results. After generating a 105,000 participant-imputed genome-wide association study (GWAS) dataset for discovery, the network enrolled and sequenced 24,998 participants. Integration of these results into the medical record and the effects of results on participants provided key lessons to the field. These learned lessons inform genetic research in diverse populations and provide insights into the clinical impact of return and implementation of genomic medicine using the electronic medical record. The lessons produced by the eMERGE Network can be utilized by other consortia as translational genomic medicine research evolves

Application of the Denovo Discrete Ordinates Radiation Transport Code to Large-Scale Fusion Neutronics

Fusion energy systems pose unique challenges to the modeling and simulation community. These challenges must be met to ensure the success of the ITER experimental fusion reactor. ITER's complex systems require detailed modeling that goes beyond the scale of comparable simulations to date. In this work, the Denovo radiation transport code was used to calculate neutron fluence and kerma for the JET streaming benchmark. This work was performed on the Titan supercomputer at the Oak Ridge Leadership Computing Facility. Denovo is a novel three-dimensional discrete ordinates transport code designed to be highly scalable. Sensitivity studies have been completed to examine the impact of several deterministic parameters. Results were compared against experiment as well as the MCNP and Shift Monte Carlo codes