Publications

The accessory Sec system is a specialized transport system that exports serine-rich repeat (SRR) glycoproteins of Gram-positive bacteria. This system contains two homologues of the general secretory (Sec) pathway (SecA2 and SecY2) and several other essential proteins (Asp1 to Asp5) that share no homology to proteins of known function. In Streptococcus gordonii, Asp2 is required for the transport of the SRR adhesin GspB, but its role in export is unknown. Tertiary structure predictions suggest that the carboxyl terminus of Asp2 resembles the catalytic region of numerous enzymes that function through a Ser-Asp-His catalytic triad. Sequence alignment of all Asp2 homologues identified a highly conserved pentapeptide motif (Gly-X-Ser(362)-X-Gly) typical of most Ser-Asp-His catalytic triads, where Ser forms the reactive residue. Site-directed mutagenesis of residues comprising the predicted catalytic triad of Asp2 of S. gordonii had no effect upon GspB transport but did result in a marked change in the electrophoretic mobility of the protein. Lectin-binding studies and monosaccharide content analysis of this altered glycoform revealed an increase in glucosamine deposition. Random mutagenesis of the Asp2 region containing this catalytic domain also disrupted GspB transport. Collectively, our findings suggest that Asp2 is a bifunctional protein that is essential for both GspB transport and correct glycosylation. The catalytic domain may be responsible for controlling the glycosylation of GspB, while other surrounding regions are functionally required for glycoprotein transport.

BACKGROUND

Previous studies on the impact of race and sex on outcome in children undergoing cardiac operations were based on analyses of administrative claims data. This study uses clinical registry data to examine potential associations of sex and race with outcomes in congenital cardiac operations, including in-hospital mortality, postoperative length of stay (LOS), and complications.

METHODS

The Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) was queried for patients younger than 18 years undergoing cardiac operations from 2007 to 2009. Preoperative, operative, and outcome data were collected on 20,399 patients from 49 centers. In multivariable analysis, the association of race and sex with outcome was examined, adjusting for patient characteristics, operative risk (Society of Thoracic Surgeons-European Association for Cardiothoracic Surgery [STAT] mortality category), and operating center.

RESULTS

Median age at operation was 0.4 years (interquartile range 0.1-3.4 years), and 54.4% of patients were boys. Race/ethnicity included 54.9% white, 17.1% black, 16.4% Hispanic, and 11.7% "other." In adjusted analysis, black patients had significantly higher in-hospital mortality (odds ratio [OR], 1.67; 95% confidence interval [CI], 1.37-2.04; p<0.001) and complication rate (OR, 1.15; 95% CI, 1.04-1.26; p<0.01) in comparison with white patients. There was no significant difference in mortality or complications by sex. Girls had a shorter LOS than boys (-0.8 days; p<0.001), whereas black (+2.4 days; p<0.001) and Hispanic patients (0.9 days; p<0.01) had longer a LOS compared with white patients.

CONCLUSIONS

These data suggest that black children have higher mortality, a longer LOS, and an increased complication rate. Girls had outcomes similar to those of boys but with a shorter LOS of almost a day. Further study of potential causes underlying these race and sex differences is warranted.

Mobile phones and devices, with their constant presence, data connectivity, and multiple intrinsic sensors, can support around-the-clock chronic disease prevention and management that is integrated with daily life. These mobile health (mHealth) devices can produce tremendous amounts of location-rich, real-time, high-frequency data. Unfortunately, these data are often full of bias, noise, variability, and gaps. Robust tools and techniques have not yet been developed to make mHealth data more meaningful to patients and clinicians. To be most useful, health data should be sharable across multiple mHealth applications and connected to electronic health records. The lack of data sharing and dearth of tools and techniques for making sense of health data are critical bottlenecks limiting the impact of mHealth to improve health outcomes. We describe Open mHealth, a nonprofit organization that is building an open software architecture to address these data sharing and "sense-making" bottlenecks. Our architecture consists of open source software modules with well-defined interfaces using a minimal set of common metadata. An initial set of modules, called InfoVis, has been developed for data analysis and visualization. A second set of modules, our Personal Evidence Architecture, will support scientific inferences from mHealth data. These Personal Evidence Architecture modules will include standardized, validated clinical measures to support novel evaluation methods, such as n-of-1 studies. All of Open mHealth's modules are designed to be reusable across multiple applications, disease conditions, and user populations to maximize impact and flexibility. We are also building an open community of developers and health innovators, modeled after the open approach taken in the initial growth of the Internet, to foster meaningful cross-disciplinary collaboration around new tools and techniques. An open mHealth community and architecture will catalyze increased mHealth efficiency, effectiveness, and innovation.