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Several studies have shown that patients suffering from Diabetes Mellitus can significantly delay the onset and slow down the progression of diabetes micro-and macro-angiopathic complications through intensive monitoring and treatment. In general, intensive treatments imply a careful blood glucose level (BGL) self-monitoring. The analysis of BGL measurements is one of the most important tasks in order to assess the glucose metabolic control and to revise the therapeutic protocol. Recent clinical studies have shown the correlation between the glucose variability and the long-term diabetes related complications. In this paper, we propose a stochastic model to extract the time course of such variability from the self-monitoring BGL time series. This information can be conveniently combined with other analysis to evaluate the adequacy of the therapeutic protocol and to highlight periods characterized by an increasing glucose instability. The method here proposed has been validated on two simulated data sets and tested with success in the retrospective analysis of three patients' data sets. Index Terms--Bayesian estimation, blood glucose level time series analysis, blood glucose variability, diabetes long-term complications, diabetic patients home monitoring, Markov chain Monte Carlo methods, risk index, stochastic volatility models.

The minimal model (MM) of glucose kinetics during an intravenous glucose tolerance test (IVGTT) is widely used in clinical studies to measure metabolic indexes such as glucose effectiveness ([S.sub.G]) and insulin sensitivity ([S.sub.I]). The standard (frequent) IVGTT sampling schedule (FSS) for MM identification consists of 30 points over 4 h. To facilitate clinical application of the MM, reduced sampling schedules (RSS) of 13-14 samples have also been derived for normal subjects. These RSS are especially appealing in large-scale studies. However, with RSS, the precision of [S.sub.G] and [S.sub.I] estimates deteriorates and, in certain cases, becomes unacceptably poor. To overcome this difficulty, population approaches such as the iterative two-stage (ITS) approach have been recently proposed, but, besides leaving some theoretical issues open, they appear to be oversized for the problem at hand. Here, we show that a Bayesian methodology operating at the single individual level allows an accurate determination of MM parameter estimates together with a credible measure of their precision. Results of 16 subjects show that, in passing from FSS to RSS, there are no significant changes of point estimates in nearly all of the subjects and that only a limited deterioration of parameter precision occurs. In addition, in contrast with the previously proposed ITS method, credible confidence intervals (e.g., excluding negative values) are obtained. They can be crucial for a subsequent use of the estimated MM parameters, such as in classification, clustering, regression, or risk analysis. identification of glucose minimal model; intravenous glucose tolerance test; normal subjects; insulin sensitivity; glucose effectiveness.

Abstract Background One of the topics of major interest in proteomics is protein identification. Protein identification can be achieved by analyzing the mass spectrum of a protein sample through different approaches. One of them, called Peptide Mass Fingerprinting (PMF), combines mass spectrometry (MS) data with searching strategies in a suitable database of known protein to provide a list of candidate proteins ranked by a score. To this aim, several algorithms and software tools have been proposed. However, the scoring methods and mainly the statistical evaluation of the results can be significantly improved. Results In this work, a Perl procedure for protein identification by PMF, called MsPI (Mass spectrometry Protein Identification), is presented. The implemented scoring methods were derived from the literature. MsPI implements a strategy to remove the contaminant masses present in the acquired spectra. Moreover, MsPI includes a statistical method to assign to each candidate protein, in addition to the scoring value, a p-value. Results obtained by MsPI on a dataset of 10 protein samples were compared with those achieved using two other software tools, i.e. Piums and Mascot. Piums implements one of the scoring methods available in MsPI, while Mascot is one of the most frequently used software tools in the protein identification field. MsPI scripts are available for downloading on the web site http://aimed11.unipv.it/MsPI. Conclusion The performances of MsPI seem to be better than those of Piums and Mascot. In fact, on the considered dataset, MsPI includes in its candidate proteins list, the "true" proteins nine times over ten, whereas Piums includes in its list the "true" proteins only four time over ten. Even if Mascot also correctly includes in the candidates list the "true" proteins nine times over ten, it provides longer candidate lists, therefore increasing the number of false positives when the molecular weight of the proteins in the sample is approximatively known (e.g. by the 1-D/2-D electrophoresis gel). Moreover, being MsPI a Perl tool, it can be easily extended and customized by the final users.

Over the last few decades, a large number of pH sensitive materials with new compositions and structures have been proposed. Solid state sensors based on organic, inorganic and composite materials are actively investigated, with an increasing interest in the performance offered by nano-scale materials. Our review provides a thorough, up-to-date knowledge of a wide range of pH measurement methods and related sensing materials, firstly by introducing well established materials and methods for pH sensing and then, by covering recent developments in inorganic, organic and nano-engineered devices. The main sensor parameters, including sensitivity, stability, response time and testing conditions are reported. Given the importance of pH sensing in environmental applications, in particular seawater monitoring, sensors tested in seawater are highlighted and discussed. [ABSTRACT FROM AUTHOR]

The minimal model of glucose kinetics, in conjunction with an insulin-modified intravenous glucose tolerance test, is widely used to estimate insulin sensitivity ([S.sub.I]). Parameter estimation usually resorts to nonlinear least squares (NLS), which provides a point estimate, and its precision is expressed as a standard deviation. Applied to type 2 diabetic subjects, NLS implemented in MINMOD software often predicts [S.sub.I]=0 (the so-called 'zero' [S.sub.I] problem), whereas general purpose modeling software systems, e.g., SAAM II, provide a very small [S.sub.I] but with a very large uncertainty, which produces unrealistic negative values in the confidence interval. To overcome these difficulties, in this article we resort to Bayesian parameter estimation implemented by a Markov chain Monte Carlo (MCMC) method. This approach provides in each individual the [S.sub.I] a posteriori probability density function, from which a point estimate and its confidence interval can be determined. Although NLS results are not acceptable in four out of the ten studied subjects, Bayes estimation implemented by MCMC is always able to determine a nonzero point estimate of [S.sub.I] together with a credible confidence interval. This Bayesian approach should prove useful in reanalyzing large databases of epidemiological studies. insulin sensitivity; insulin resistance; mathematical model; parameter estimation; type 2 diabetes