Serological Response to Pasteurella multocida NanH Sialidase in Persistently Colonized Rabbits

Pasteurella multocida is a mucosal pathogen that colonizes the upper respiratory system of rabbits. Respiratory infections can result, but the bacteria can also invade the circulatory system, producing abscesses or septicemia. P. multocida produces extracellular sialidase activity, which is believed to augment colonization of the respiratory tract and the production of lesions in an active infection. Previously, it was demonstrated that some isolates of P. multocida contain two unique sialidase genes, nanH and nanB, that encode enzymes with different substrate specificities (S. Mizan, A. D. Henk, A. Stallings, M. Meier, J. J. Maurer, and M. D. Lee, J. Bacteriol. 182:6874-6883, 2000). We developed a recombinant antigen enzyme-linked immunosorbent assay (ELISA) based on the NanH sialidase of P. multocida and demonstrated that rabbits that were experimentally colonized with P. multocida produce detectable anti-NanH immunoglobulin M (IgM) and IgG in serum, although they demonstrated no clinical signs of pasteurellosis. In addition, clinically ill pet rabbits infected with P. multocida possessed IgM and/or IgG antibody against NanH. The NanH ELISA may be useful for the diagnosis of P. multocida infections in sick rabbits as well as for screening for carriers in research rabbit colonies

The Decade Show : Frameworks of Identity in the 1980s

The authors examine American artistic practices in the 1980s, emphasizing the histories and work of Asian, Latin, African and Native Americans, women and gays, whose voices have traditionally been suppressed by white male middleclass domination. Includes references to ethnocentrism, deconstruction, democracy, activism, the environment, homelessness, AIDS, video and performance art. Also includes a chronology of the 1980s, and brief biographical notes. Circa 200 bibl. ref

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma.

BackgroundCancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.MethodsTissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.ResultsOur computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).ConclusionsThese proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy