Serum antibodies to oral anaerobic bacteria in patients with rheumatoid arthritis

BACKGROUND: This study was conducted to determine the component that causes the disease in rheumatoid arthritis (RA), which shows great resemblance to periodontitis in a pathologic context. MATERIALS AND METHODS: Within this study, the pathogen-specific IgG levels formed against Porphyromonas gingivalis FDC 381, Prevotella melaninogenica ATCC 25845, Actinobacillus actinomycetemcomitans Y4, Bacteroides forsythus ATCC 43047, and Prevotella intermedia 25611 oral bacteria were researched from the blood serum samples of 30 RA patients and 20 healthy controls with the enzyme-linked immunosorbent assay (ELISA) method. RESULTS: The IgG levels of P gingivalis, P intermedia, P melaninogenica, and B forsythus were found to be significantly higher in RA patients when compared with those of the controls. Of the other bacteria antibodies, A actinomycetemcomitans was not found at greater levels in RA serum samples in comparison with the healthy samples. CONCLUSION: The antibodies formed against P gingivalis, P intermedia, P melaninogenica, and B forsythus could be important to the etiopathogenesis of RA.

Simulation of binary mixture adsorption of methane and CO2 at supercritical conditions in carbons

Knowledge of the adsorption behavior of coal-bed gases, mainly under supercritical high-pressure conditions, is important for optimum design of production processes to recover coal-bed methane and to sequester CO2 in coal-beds. Here, we compare the two most rigorous adsorption methods based on the statistical mechanics approach, which are Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulation, for single and binary mixtures of methane and carbon dioxide in slit-shaped pores ranging from around 0.75 to 7.5 nm in width, for pressure up to 300 bar, and temperature range of 308-348 K, as a preliminary study for the CO2 sequestration problem. For single component adsorption, the isotherms generated by DFT, especially for CO2, do not match well with GCMC calculation, and simulation is subsequently pursued here to investigate the binary mixture adsorption. For binary adsorption, upon increase of pressure, the selectivity of carbon dioxide relative to methane in a binary mixture initially increases to a maximum value, and subsequently drops before attaining a constant value at pressures higher than 300 bar. While the selectivity increases with temperature in the initial pressure-sensitive region, the constant high-pressure value is also temperature independent. Optimum selectivity at any temperature is attained at a pressure of 90-100 bar at low bulk mole fraction of CO2, decreasing to approximately 35 bar at high bulk mole fractions. (c) 2005 American Institute of Chemical Engineers.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.