Browsing by Author "Huang, Erich S"

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    A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies.
    (Frontiers in microbiology, 2018-01) Poore, Gregory D; Ko, Emily R; Valente, Ashlee; Henao, Ricardo; Sumner, Kelsey; Hong, Christopher; Burke, Thomas W; Nichols, Marshall; McClain, Micah T; Huang, Erich S; Ginsburg, Geoffrey S; Woods, Christopher W; Tsalik, Ephraim L
    Background: Acute respiratory infections (ARIs) are the leading indication for antibacterial prescriptions despite a viral etiology in the majority of cases. The lack of available diagnostics to discriminate viral and bacterial etiologies contributes to this discordance. Recent efforts have focused on the host response as a source for novel diagnostic targets although none have explored the ability of host-derived microRNAs (miRNA) to discriminate between these etiologies. Methods: In this study, we compared host-derived miRNAs and mRNAs from human H3N2 influenza challenge subjects to those from patients with Streptococcus pneumoniae pneumonia. Sparse logistic regression models were used to generate miRNA signatures diagnostic of ARI etiologies. Generalized linear modeling of mRNAs to identify differentially expressed (DE) genes allowed analysis of potential miRNA:mRNA relationships. High likelihood miRNA:mRNA interactions were examined using binding target prediction and negative correlation to further explore potential changes in pathway regulation in response to infection. Results: The resultant miRNA signatures were highly accurate in discriminating ARI etiologies. Mean accuracy was 100% [88.8-100; 95% Confidence Interval (CI)] in discriminating the healthy state from S. pneumoniae pneumonia and 91.3% (72.0-98.9; 95% CI) in discriminating S. pneumoniae pneumonia from influenza infection. Subsequent differential mRNA gene expression analysis revealed alterations in regulatory networks consistent with known biology including immune cell activation and host response to viral infection. Negative correlation network analysis of miRNA:mRNA interactions revealed connections to pathways with known immunobiology such as interferon regulation and MAP kinase signaling. Conclusion: We have developed novel human host-response miRNA signatures for bacterial and viral ARI etiologies. miRNA host response signatures reveal accurate discrimination between S. pneumoniae pneumonia and influenza etiologies for ARI and integrated analyses of the host-pathogen interface are consistent with expected biology. These results highlight the differential miRNA host response to bacterial and viral etiologies of ARI, offering new opportunities to distinguish these entities.
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    Application programming interfaces for knowledge transfer and generation in the life sciences and healthcare.
    (NPJ digital medicine, 2020-01) Woody, Stephen K; Burdick, David; Lapp, Hilmar; Huang, Erich S
    Storing very large amounts of data and delivering them to researchers in an efficient, verifiable, and compliant manner, is one of the major challenges faced by health care providers and researchers in the life sciences. The electronic health record (EHR) at a hospital or clinic currently functions as a silo, and although EHRs contain rich and abundant information that could be used to understand, improve, and learn from care as part learning health system access to these data is difficult, and the technical, legal, ethical, and social barriers are significant. If we create a microservice ecosystem where data can be accessed through APIs, these challenges become easier to overcome: a service-driven design decouples data from clients. This decoupling provides flexibility: different users can write in their preferred language and use different clients depending on their needs. APIs can be written for iOS apps, web apps, or an R library, and this flexibility highlights the potential ecosystem-building power of APIs. In this article, we use two case studies to illustrate what it means to participate in and contribute to interconnected ecosystems that powers APIs in a healthcare systems.
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    Erratum: Publisher Correction: Application programming interfaces for knowledge transfer and generation in the life sciences and healthcare.
    (NPJ digital medicine, 2020-01) Woody, Stephen K; Burdick, David; Lapp, Hilmar; Huang, Erich S
    [This corrects the article DOI: 10.1038/s41746-020-0235-5.].
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    The Project Baseline Health Study: a step towards a broader mission to map human health.
    (NPJ digital medicine, 2020-01) Arges, Kristine; Assimes, Themistocles; Bajaj, Vikram; Balu, Suresh; Bashir, Mustafa R; Beskow, Laura; Blanco, Rosalia; Califf, Robert; Campbell, Paul; Carin, Larry; Christian, Victoria; Cousins, Scott; Das, Millie; Dockery, Marie; Douglas, Pamela S; Dunham, Ashley; Eckstrand, Julie; Fleischmann, Dominik; Ford, Emily; Fraulo, Elizabeth; French, John; Gambhir, Sanjiv S; Ginsburg, Geoffrey S; Green, Robert C; Haddad, Francois; Hernandez, Adrian; Hernandez, John; Huang, Erich S; Jaffe, Glenn; King, Daniel; Koweek, Lynne H; Langlotz, Curtis; Liao, Yaping J; Mahaffey, Kenneth W; Marcom, Kelly; Marks, William J; Maron, David; McCabe, Reid; McCall, Shannon; McCue, Rebecca; Mega, Jessica; Miller, David; Muhlbaier, Lawrence H; Munshi, Rajan; Newby, L Kristin; Pak-Harvey, Ezra; Patrick-Lake, Bray; Pencina, Michael; Peterson, Eric D; Rodriguez, Fatima; Shore, Scarlet; Shah, Svati; Shipes, Steven; Sledge, George; Spielman, Susie; Spitler, Ryan; Schaack, Terry; Swamy, Geeta; Willemink, Martin J; Wong, Charlene A
    The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis.