Depletion of B2 but Not B1a B Cells in BAFF Receptor-Deficient ApoE−/− Mice Attenuates Atherosclerosis by Potently Ameliorating Arterial Inflammation

We have recently identified conventional B2 cells as atherogenic and B1a cells as atheroprotective in hypercholesterolemic ApoE−/− mice. Here, we examined the development of atherosclerosis in BAFF-R deficient ApoE−/− mice because B2 cells but not B1a cells are selectively depleted in BAFF-R deficient mice. We fed BAFF-R−/− ApoE−/− (BaffR.ApoE DKO) and BAFF-R+/+ApoE−/− (ApoE KO) mice a high fat diet (HFD) for 8-weeks. B2 cells were significantly reduced by 82%, 81%, 94%, 72% in blood, peritoneal fluid, spleen and peripheral lymph nodes respectively; while B1a cells and non-B lymphocytes were unaffected. Aortic atherosclerotic lesions assessed by oil red-O stained-lipid accumulation and CD68+ macrophage accumulation were decreased by 44% and 50% respectively. B cells were absent in atherosclerotic lesions of BaffR.ApoE DKO mice as were IgG1 and IgG2a immunoglobulins produced by B2 cells, despite low but measurable numbers of B2 cells and IgG1 and IgG2a immunoglobulin concentrations in plasma. Plasma IgM and IgM deposits in atherosclerotic lesions were also reduced. BAFF-R deficiency in ApoE−/− mice was also associated with a reduced expression of VCAM-1 and fewer macrophages, dendritic cells, CD4+ and CD8+ T cell infiltrates and PCNA+ cells in lesions. The expression of proinflammatory cytokines, TNF-α, IL1-β and proinflammatory chemokine MCP-1 was also reduced. Body weight and plasma cholesterols were unaffected in BaffR.ApoE DKO mice. Our data indicate that B2 cells are important contributors to the development of atherosclerosis and that targeting the BAFF-R to specifically reduce atherogenic B2 cell numbers while preserving atheroprotective B1a cell numbers may be a potential therapeutic strategy to reduce atherosclerosis by potently reducing arterial inflammation

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Multiparameter Intelligent Monitoring in Intensive Care Ii (Mimic-Ii): A Public-Access Intensive Care Unit Database

Objective: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Design: Data collection and retrospective analysis. Setting: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Patients: Adult patients admitted to intensive care units between 2001 and 2007. Interventions: None. Measurements and Main Results: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). Conclusions: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.National Institute for Biomedical Imaging and Bioengineering (U.S.) (grant R01 EB001659