Continuous Flow Analysis of labile iron in ice-cores

The important active and passive role of mineral dust aerosol in the climate and the global carbon cycle over the last glacial/interglacial cycles has been recognized. However, little data on the most important aeolian dust-derived biological micronutrient, iron (Fe), has so far been available from ice-cores from Greenland or Antarctica. Furthermore, Fe deposition reconstructions derived from the palaeoproxies particulate dust and calcium differ significantly from the Fe flux data available. The ability to measure high temporal resolution Fe data in polar ice-cores is crucial for the study of the timing and magnitude of relationships between geochemical events and biological responses in the open ocean. This work adapts an existing flow injection analysis (FIA) methodology for low-level trace Fe determinations with an existing glaciochemical analysis system, continuous flow analysis (CFA) of ice-cores. Fe-induced oxidation of N,N′-dimethyl-p-pheylenediamine (DPD) is used to quantify the biologically more important and easily leachable Fe fraction released in a controlled digestion step at pH ∼1.0. The developed method was successfully applied to the determination of labile Fe in ice-core samples collected from the Antarctic Byrd ice-core and the Greenland Ice-Core Project (GRIP) ice-core.

Identification of pathogen and host-response markers correlated with periodontal disease

BACKGROUND: Periodontitis is the major cause of tooth loss in adults and is linked to systemic illnesses, such as cardiovascular disease and stroke. The development of rapid point-of-care (POC) chairside diagnostics has the potential for the early detection of periodontal infection and progression to identify incipient disease and reduce health care costs. However, validation of effective diagnostics requires the identification and verification of biomarkers correlated with disease progression. This clinical study sought to determine the ability of putative host- and microbially derived biomarkers to identify periodontal disease status from whole saliva and plaque biofilm. METHODS: One hundred human subjects were equally recruited into a healthy/gingivitis group or a periodontitis population. Whole saliva was collected from all subjects and analyzed using antibody arrays to measure the levels of multiple proinflammatory cytokines and bone resorptive/turnover markers. RESULTS: Salivary biomarker data were correlated to comprehensive clinical, radiographic, and microbial plaque biofilm levels measured by quantitative polymerase chain reaction (qPCR) for the generation of models for periodontal disease identification. Significantly elevated levels of matrix metalloproteinase (MMP)-8 and -9 were found in subjects with advanced periodontitis with Random Forest importance scores of 7.1 and 5.1, respectively. The generation of receiver operating characteristic curves demonstrated that permutations of salivary biomarkers and pathogen biofilm values augmented the prediction of disease category. Multiple combinations of salivary biomarkers (especially MMP-8 and -9 and osteoprotegerin) combined with red-complex anaerobic periodontal pathogens (such as Porphyromonas gingivalis or Treponema denticola) provided highly accurate predictions of periodontal disease category. Elevated salivary MMP-8 and T. denticola biofilm levels displayed robust combinatorial characteristics in predicting periodontal disease severity (area under the curve = 0.88; odds ratio = 24.6; 95% confidence interval: 5.2 to 116.5). CONCLUSIONS: Using qPCR and sensitive immunoassays, we identified host- and bacterially derived biomarkers correlated with periodontal disease. This approach offers significant potential for the discovery of biomarker signatures useful in the development of rapid POC chairside diagnostics for oral and systemic diseases. Studies are ongoing to apply this approach to the longitudinal predictions of disease activity.