Cross-reactivity of GroEL antibodies with human heat shock protein 60 and quantification of pathogens in atherosclerosis

Background/aims: Chronic infections such as those caused by Chlamydia pneumoniae and periodontopathic bacteria such as Porphyromonas gingivalis have been associated with atherosclerosis, possibly due to cross-reactivity of the immune response to bacterial GroEL with human heat shock protein (hHSP) 60. Methods: We examined the cross-reactivity of anti-GroEL and anti-P. gingivalis antibodies with hHSP60 in atherosclerosis patients and quantified a panel of six pathogens in atheromas. Results: After absorption of plasma samples with hHSP60, there were variable reductions in the levels of anti-GroEL and anti-P. gingivalis antibodies, suggesting that these antibodies cross-reacted with hHSP60. All of the artery specimens were positive for P. gingivalis. Fusobacterium nucleatum, Tannerella forsythia, C. pneumoniae, Helicobacter pylori, and Haemophilus influenzae were found in 84%, 48%, 28%, 4%, and 4% of arteries, respectively. The prevalence of the three periodontopathic microorganisms, P. gingivalis, F. nucleatum and T. forsythia, was significantly higher than that of the remaining three microorganisms. Conclusions: These results support the hypothesis that in some patients, cross-reactivity of the immune response to bacterial HSPs including those of periodontal pathogens, with arterial endothelial cells expressing hHSP60 may be a possible mechanism for the association between atherosclerosis and periodontal infection.

Peptide quantification by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry: Investigations of the cyclotide kalata B1 in biological fluids

A rapid method has been developed for the quantification of the prototypic cyclotide kalata B I in water and plasma utilizing matrix-assisted laser desorption ionisation time-of-flight (MALDI-TOF) mass spectrometry. The unusual structure of the cyclotides means that they do not ionise as readily as linear peptides and as a result of their low ionisation efficiency, traditional LC/MS analyses were not able to reach the levels of detection required for the quantification of cyclotides in plasma for pharmacokinetic studies. MALDI-TOF-MS analysis showed linearity (R-2 > 0.99) in the concentration range 0.05-10 mu g/mL with a limit of detection of 0.05 mu g/mL (9 fmol) in plasma. This paper highlights the applicability of MALDI-TOF mass spectrometry for the rapid and sensitive quantification of peptides in biological samples without the need for extensive extraction procedures. (c) 2005 Elsevier B.V. All rights reserved.

Quantification of lean bodyweight

Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5kg) and BMI values (17.1-69.9 kg/m(2)). Patients in population B had BMI values of 18.7-38.4 kg/m(2). A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r(2) = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r(2) = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r(2) = 0.85, ME -0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

Lifetime prevalence estimates of major depression: An indirect estimation method and a quantification of recall bias

The measurement of lifetime prevalence of depression in cross-sectional surveys is biased by recall problems. We estimated it indirectly for two countries using modelling, and quantified the underestimation in the empirical estimate for one. A microsimulation model was used to generate population-based epidemiological measures of depression. We fitted the model to 1-and 12-month prevalence data from the Netherlands Mental Health Survey and Incidence Study (NEMESIS) and the Australian Adult Mental Health and Wellbeing Survey. The lowest proportion of cases ever having an episode in their life is 30% of men and 40% of women, for both countries. This corresponds to a lifetime prevalence of 20 and 30%, respectively, in a cross-sectional setting (aged 15-65). The NEMESIS data were 38% lower than these estimates. We conclude that modelling enabled us to estimate lifetime prevalence of depression indirectly. This method is useful in the absence of direct measurement, but also showed that direct estimates are underestimated by recall bias and by the cross-sectional setting.

The evolution of the Global Burden of Disease framework for disease, injury and risk factor quantification: Developing the evidence base for national, regional and global public health action

Reliable, comparable information about the main causes of disease and injury in populations, and how these are changing, is a critical input for debates about priorities in the health sector. Traditional sources of information about the descriptive epidemiology of diseases, injuries and risk factors are generally incomplete, fragmented and of uncertain reliability and comparability. Lack of a standardized measurement framework to permit comparisons across diseases and injuries, as well as risk factors, and failure to systematically evaluate data quality have impeded comparative analyses of the true public health importance of various conditions and risk factors. As a consequence the impact of major conditions and hazards on population health has been poorly appreciated, often leading to a lack of public health investment. Global disease and risk factor quantification improved dramatically in the early 1990s with the completion of the first Global Burden of Disease Study. For the first time, the comparative importance of over 100 diseases and injuries, and ten major risk factors, for global and regional health status could be assessed using a common metric (Disability-Adjusted Life Years) which simultaneously accounted for both premature mortality and the prevalence, duration and severity of the non-fatal consequences of disease and injury. As a consequence, mental health conditions and injuries, for which non-fatal outcomes are of particular significance, were identified as being among the leading causes of disease/injury burden worldwide, with clear implications for policy, particularly prevention. A major achievement of the Study was the complete global descriptive epidemiology, including incidence, prevalence and mortality, by age, sex and Region, of over 100 diseases and injuries. National applications, further methodological research and an increase in data availability have led to improved national, regional and global estimates for 2000, but substantial uncertainty around the disease burden caused by major conditions, including, HIV, remains. The rapid implementation of cost-effective data collection systems in developing countries is a key priority if global public policy to promote health is to be more effectively informed.

Direct quantification of rare earth element concentrations in natural waters by ICP-MS

A direct quadrupole ICP-MS technique has been developed for the analysis of the rare earth elements and yttrium in natural waters. The method has been validated by comparison of the results obtained for the river water reference material SLRS-4 with literature values. The detection limit of the technique was investigated by analysis of serial dilutions of SLRS-4 and revealed that single elements can be quantified at single-digit fg/g concentrations. A coherent normalised rare earth pattern was retained at concentrations two orders of magnitude below natural concentrations for SLRS-4, demonstrating the excellent inter-element accuracy and precision of the method. The technique was applied to the analysis of a diluted mid-salinity estuarine sample, which also displayed a coherent normalised rare earth element pattern, yielding the expected distinctive marine characteristics. (c) 2006 Published by Elsevier Ltd.

Rapid quantification of hepatitis B virus DNA by real-time PCR using efficient TaqMan probe and extraction of virus DNA

Aim: To rapidly quantify hepatitis B virus (HBV) DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. Methods: Three standards were prepared by cloning PCR products which targeted S, C and X region of HBV genome into pGEM-T vector respectively. A pair of primers and matched TaqMan probe were selected by comparing the copy number and the Ct values of HBV serum samples derived from the three different standard curves using certain serum DNA. Then the efficiency of six HBV DNA extraction methods including guanidinium isothiocyanate, proteinase K, NaI, NaOH lysis, alkaline lysis and simple boiling was analyzed in sample A, B and C by real-time PCR. Meanwhile, 8 clinical HBV serum samples were quantified. Results: The copy number of the same HBV serum sample originated from the standard curve of S, C and X regions was 5.7 × 104/ mL, 6.3 × 102/mL and 1.6 × 103/mL respectively. The relative Ct value was 26.6, 31.8 and 29.5 respectively. Therefore, primers and matched probe from S region were chosen for further optimization of six extraction methods. The copy number of HBV serum samples A, B and C was 3.49 × 109/mL, 2.08 × 106/mL and 4.40 × 107/mL respectively, the relative Ct value was 19.9, 30 and 26.2 in the method of NaOH lysis, which was the efficientest among six methods. Simple boiling showed a slightly lower efficiency than NaOH lysis. Guanidinium isothiocyanate, proteinase K and NaI displayed that the copy number of HBV serum sample A, B and C was around 105/ mL, meanwhile the Ct value was about 30. Alkaline failed to quantify the copy number of three HBV serum samples, Standard deviation (SD) and coefficient variation (CV) were very low in all 8 clinical HBV serum samples, showing that quantification of HBV DNA in triplicate was reliable and accurate. Conclusion: Real-time PCR based on optimized primers and TaqMan probe from S region in combination with NaOH lysis is a simple, rapid and accurate method for quantification of HBV serum DNA. © 2006 The WJG Press. All rights reserved.

Quantification of cellulase activity using cellulose-azure

Despite wide application of cellulose-azure as a substrate for measuring cellulase activity, there is no quantification of hydrolysis rate or enzymatic activities using this substrate. The aim of this study was to quantify the hydrolysis rate in terms of product formation and dye released using cellulose-azure. The amount of dye released was correlated with the production of glucose and the enzyme concentrations. It is shown that the lack of correlation can be due to (1) repression of the release of the azure-dye when azure-dye accumulates, (2) presence of degradable substrates in the cellulase powder which inflate the glucose measurements and (3) the degradation of cellulose which is not linked to the dye in the cellulose-azure. Based on the lack of correlation, it is recommended that cellulose-azure should only be applied in assays when the aim is to compare relative activities of different enzymatic systems. (c) 2005 Elsevier B.V. All rights reserved.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry