Association of tracking oral hygiene with dental outcomesin a general dental practice.

PURPOSE: To track plaque scores on a subset of teeth in general dental practice patients to determine if plaque scores could improve along with periodontal, restorative and extraction outcomes. METHODS: Percentage of surfaces with subgingival plaque were recorded and graphed on five teeth (#3, 8, 14, 19, 30) at each appointment, followed by focused oral hygiene instructions, in 343 patients over a 5-10-year period. Patient age, gender, prophylaxes/year, and experimental teeth periodontitis stage, % 4-5 and ≥ 6 mm pockets, % bleeding on probing, % surfaces restored and patients with extractions were recorded. Relationships among average plaque scores and the longitudinal periodontal, restorative and extraction changes were analyzed using Chi-Square, Kruskal-Wallis, and Wilcoxon Rank Sum tests. RESULTS: Plaque scores improved from median 40% to 25% (P< 0.0001) over the 5-10 years. Plaque scores and periodontitis stages were associated (P= 0.03) with few periodontally healthy patients (9%) having poor plaque scores (> 50% plaque surfaces). Furthermore, good plaque scores (≤ 25%) and periodontal health (Stage I) were linked to the need for few restorations (P< 0.0001), while prophylaxes/year had no significant relationship. Extractions were related more with Stage III/IV (advanced) periodontitis (P< 0.0001) than with plaque score (NS). CLINICAL SIGNIFICANCE: In a general dental practice, tracking plaque scores at each appointment on a subset of representative teeth can be time-efficient, and is associated with improved oral hygiene, stable periodontal status and reduced restorative needs.

Molecular phylogeny and evolutionary dynamics of influenza A nonstructural (NS) gene.

While the nonstructural gene (NS) of the influenza A virus plays a crucial role in viral virulence and replication, the complete understanding of its molecular phylogeny and evolutionary dynamics remains lacking. In this study, the phylogenetic analysis of 7581 NS sequences revealed ten distinct lineages within alleles A and B: three host-specific (human, classical swine and equine), two reassortment-originated (A(H1N1)pdm09 and triple reassortment swine), one transmission-originated (Eurasian swine), and two geographically isolated avian (Eurasian/Oceanian and North American) for allele A and two geographically isolated avian (Eurasian/Oceanian and North American) for allele B. The average nucleotide substitution rates of the lineages range from 1.24×10(-3) (equine) to 4.34×10(-3) (A(H1N1)pdm09) substitutions per site per year. The selection pressure analysis demonstrated that the dN/dS ratio of the NS gene in A(H1N1)pdm09 lineage was higher than its closely related triple reassortant swine, which could be attributed to the adaptation to the new host and/or intensive surveillance after the inter-species transmission from swine to human. The positive selection sites were found in all lineages except the equine lineage and mostly in the NS1 region. The positive selection sites 22, 26, 226, 227 and 230 of the human lineage are significant because these residues participate in either forming the dimerization of the two RNA binding domain (RBD) monomers or blocking the replication of host genes. Residues at position 171 provide hydrophobic interactions with hydrophobic residues at p85ß and thus induce viral cell growth. The lineages and evolutionary dynamics of influenza A NS gene obtained in this study, along with the studies of other gene segments, are expected to improve the early detection of new viruses and thus have the potential to enhance influenza surveillance.

Dynamics of influenza virus and human host interactions during infection and replication cycle.

The replication and life cycle of the influenza virus is governed by an intricate network of intracellular regulatory events during infection, including interactions with an even more complex system of biochemical interactions of the host cell. Computational modeling and systems biology have been successfully employed to further the understanding of various biological systems, however, computational studies of the complexity of intracellular interactions during influenza infection is lacking. In this work, we present the first large-scale dynamical model of the infection and replication cycle of influenza, as well as some of its interactions with the host's signaling machinery. Specifically, we focus on and visualize the dynamics of the internalization and endocytosis of the virus, replication and translation of its genomic components, as well as the assembly of progeny virions. Simulations and analyses of the models dynamics qualitatively reproduced numerous biological phenomena discovered in the laboratory. Finally, comparisons of the dynamics of existing and proposed drugs, our results suggest that a drug targeting PB1:PA would be more efficient than existing Amantadin/Rimantaine or Zanamivir/Oseltamivir.

Bio-logic builder: a non-technical tool for building dynamical, qualitative models.

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized "bio-logic" modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.

The Cell Collective: toward an open and collaborative approach to systems biology.

BACKGROUND: Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety. RESULTS: The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time. CONCLUSIONS: The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction.