Background

Race and socioeconomic status influence outcomes for adult and pediatric burn patients, yet the impact of these factors on elderly patients (Medicare eligible, 65 years of age) remains unknown.

Methods

Data pooled from three verified burn centers from 2004 to 2014 were reviewed retrospectively. Age, race, gender, percent total body surface area (%TBSA) burn, mortality, length of stay (LOS), LOS per %TBSA burn, and zip code which provided Census data on race, poverty, and education levels within a community were collected. Data were analyzed using logistic and generalized linear models in SAS version 9.4 (SAS Institute, Cary, NC, USA).

Results

Our population was mainly Caucasian (63%), African American (18%), Hispanic (7.6%), and Asian (3.5%). Mean age was 76.3 ± 8.3 years, 52.5% were male. Mean %TBSA was 9 ± 13.8%; 15% of the patients sustained an inhalation injury. The mortality rate was 14.4%. Inhalation injury was significantly associated with mortality and discharge to a skilled nursing facility (SNF) (p < 0.05). Race was significantly associated with socioeconomic disparities and affected LOS/TBSA, but not discharge to SNF or mortality on univariate analysis. Poverty level, education level, and insurance status (others vs. public) independently predicted SNF discharge, while median income and insurance type independently predicted LOS/TBSA.

Conclusion

In this elderly cohort, race did not predict standard markers of burn outcome (mortality and discharge to SNF). Socioeconomic status independently predicted LOS and discharge to SNF, suggesting a relationship between socioeconomic status and recovery from a burn injury. Better understanding of racial and socioeconomic disparities is necessary to provide equitable treatment of all patients.

Environmental sensor networks are now commonly being deployed within environmental observatories and as components of smaller-scale ecological and environmental experiments. Effectively using data from these sensor networks presents technical challenges that are difficult for scientists to overcome, severely limiting the adoption of automated sensing technologies in environmental science. The Realtime Environment for Analytical Processing (REAP) is an NSF-funded project to address the technical challenges related to accessing and using heterogeneous sensor data from within the Kepler scientific workflow system. Using distinct use cases in terrestrial ecology and oceanography as motivating examples, we describe workflows and extensions to Kepler to stream and analyze data from observatory networks and archives. We focus on the use of two newly integrated data sources in Kepler: DataTurbine and OPeNDAP. Integrated access to both near real-time data streams and data archives from within Kepler facilitates both simple data exploration and sophisticated analysis and modeling with these data sources.

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.