Kirsten Taylor-McCabe

Immunophenotyping of chicken peripheral blood lymphocyte subpopulations: Individual variability and repeatability

T-cell lymphocyte populations can be delineated into subsets based on expression of cell surface proteins that can be measured in peripheral blood by monoclonal antibodies and flow cytometry percentages of the lymphocyte subpopulations. In order to accurately assess immunocompetence in birds, natural variability in both avian immune function and the methodology must be understood. Our objectives were to (1) further develop flow cytometry for estimating subpopulations of lymphocytes in peripheral blood from poultry, (2) estimate repeatability and variability in the methodology with respect to poultry in a free-range and environmentally diverse situation, and (3) estimate the best antibody and cell marker combination for estimating lymphocyte subpopulations. This work demonstrated the repeatability of using flow cytometry for measurements of peripheral blood in chickens using anti-chicken antibodies for lymphocyte subpopulations. Immunofluorescence staining of cells isolated from peripheral blood revealed that the CD3(+) antibodies reacted with an average of approximately 12-24% of the lymphoid cells in the blood, depending on the fluorescence type. The CD4(+) and CD8(+) molecules were expressed in a range of 4-31% and 1-10% of the lymphoid cells in the blood, respectively. Both fluorescence label and antibody company contribute to the variability of results and should be considered in future flow cytometry studies in poultry.

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

Clinical and acquired immunologic responses to West Nile virus infection of domestic chickens (Gallus gallus domesticus)

Numerous bird species are highly susceptible to North American strains of West Nile virus (WNV), and although domestic chickens are relatively resistant to WNV-associated disease, this species currently represents the most practical avian model for immune responses to WNV infection. Knowledge of the immunomodulation of susceptibility to WNV in birds is important for understanding taxonomic differences in infection outcomes. While focusing on immunophenotyping of CD3(+), CD4(+), CD8(+), and CD45(+) lymphocyte subpopulations, we compared lymphocyte subpopulations, blood chemistries, cloacal temperatures, IgM and IgG antibody titers, and differential whole-blood cell counts of WNV-infected and uninfected hens. Total blood calcium and lymphocyte numbers were lower in WNV-infected chickens compared with uninfected chickens. The heterophil-to-lymphocyte ratio increased over time from 2 to 22 d postinoculation (DPI) in uninfected chickens and from 2 to 8 DPI in WNV-infected chickens, although levels declined from 8 to 22 DPI in the latter group. No significant differences were found in the remaining immunological and hematological variables of the WNV-infected and uninfected groups. Our results reaffirm that chickens are resistant to WNV infection, and demonstrated that the heterophil-to-lymphocyte ratio differed between groups, allowing for sorting of infection status. Similar patterns in immune responses over time in both infected and uninfected hens may be related to age (i.e., 10 wk) and associated immune development.

The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Selecting essential information for biosurveillance--a multi-criteria decision analysis.

The National Strategy for Biosurveillancedefines biosurveillance as “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” However, the strategy does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.