Hepatobiliary excretion and brain distribution of caffeine in rats using microdialysis

To investigate the pharmacokinetic mechanism of hepatobiliary excretion and brain distribution of caffeine, this study uses a method based on microdialysis technique and liquid chromatography that allows continuous and concurrent in vivo monitoring of extracellular caffeine in the blood, brain and bile of anesthetized rats following the administration of caffeine (3 or 10 mg/kg, i.v.) through the femoral vein. Dialysates of the blood, brain and bile were directly injected onto the liquid chromatographic system and no further clean-up procedures were required. The study design consisted of two groups of six rats in parallel: the rats of the control group received caffeine (3 or 10 mg/kg, i.v.) alone and those of the cyclosporine treated-group were injected cyclosporine (10 mg/kg, i.v.) 10 min prior to caffeine administration (3 or 10 mg/kg, i.v.). The decline of caffeine in the blood, brain striatum and bile suggested that caffeine had rapid exchange and equilibration between the peripheral compartment and the central nervous system. In addition, the results indicated that caffeine underwent hepatobiliary excretion and was distributed into brain. When cyclosporine was co-administered, the pharmacokinetic parameters were not significantly altered. The results of this study reveal that the pharmacokinetic mechanism of hepatobiliary excretion and brain distribution of caffeine might not relate to P-glycoprotein.     

A characteristic finite element method with local mesh refinements for the Lamm equation in analytical ultracentrifugation

The Lamm equation is a fundamental differential equation in analytical ultracentrifugation, for describing the transport of solutes in an ultracentrifuge cell. In this article, we present a characteristic finite element method with local mesh refinements for solving the Lamm equation. The numerical method is mass‐conservative by design and allows relatively large time steps to be used. Numerical experiments indicate that the numerical solutions are oscillation‐free in the region near the cell bottom, where mass build up and large concentration gradients occur. Positivity of solutions is also well kept. © 2008 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2009     

A tutorial on support vector machine-based methods for classification problems in chemometrics

This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.     

Osteoporosis detection in postmenopausal women using axial transmission multi-frequency bone ultrasonometer: Clinical findings

The objective of this study was to evaluate if the Bone UltraSonic Scanner (BUSS) can detect osteoporosis in postmenopausal women. BUSS is an axial transmission multi-frequency ultrasonometer for acquisition of wave propagation profiles along the proximal anterior tibia. We derived 10 diagnostically significant BUSS parameters that were then compared with the DXA spine T-score, which was used in this study as the “gold standard” for the assessment of osteoporosis (T-score <−2.5). BUSS wave parameters were studied in 331 postmenopausal women examined by 9 trained operators at 3 clinical sites with use of 3 devices. The efficiency of each BUSS parameter in osteoporosis detection was assessed using a receiver operating characteristic curve analysis. Area under the curve (AUC) for each of 10 parameters ranged from 58.1% to 70.2%. Using these parameters a linear classifier was derived which provided at its output 83.0% AUC, 87.7% sensitivity and 63.2% specificity to DXA-identified osteoporosis. The results of this study confirm BUSS’s capability to detect osteoporosis in postmenopausal women.     

What do we know and when do we know it?

Two essential aspects of virtual screening are considered: experimental design and performance metrics. In the design of any retrospective virtual screen, choices have to be made as to the purpose of the exercise. Is the goal to compare methods? Is the interest in a particular type of target or all targets? Are we simulating a ‘real-world’ setting, or teasing out distinguishing features of a method? What are the confidence limits for the results? What should be reported in a publication? In particular, what criteria should be used to decide between different performance metrics? Comparing the field of molecular modeling to other endeavors, such as medical statistics, criminology, or computer hardware evaluation indicates some clear directions. Taken together these suggest the modeling field has a long way to go to provide effective assessment of its approaches, either to itself or to a broader audience, but that there are no technical reasons why progress cannot be made.     

Normalization using a tagged-internal standard assay for analysis of antibody arrays and the evaluation of serological biomarkers for liver disease

For minimizing systemic experimental variation in the analysis of antibody array data, we developed a novel median-centered/IgM-tagged-internal standard (TIS) assay normalization using median-centering and TIS assay-based determination of serum IgM concentrations. We evaluated five normalization methods by analyzing correlation coefficients and coefficients of variation for six serum proteins using human serum samples from normal controls (n = 25) and patients with liver cirrhosis (n = 25) or hepatocellular carcinoma (HCC; n = 29). Median-centered normalization improved correlation coefficients, while IgM-based normalizations improved coefficients of variation. The TIS assay was more efficient, economical, and reproducible for determining IgM concentrations than enzyme-linked immunosorbent assay. Additionally, we normalized antibody array data for six serum proteins using the median-centered/IgM-TIS assay, and evaluated serum biomarkers through distribution analysis of normalized fluorescence intensities and receiver operating characteristic analyses for the diagnosis of liver cirrhosis and HCC. Apolipoprotein A-1 and a combination of alpha-fetoprotein and C-reactive protein were determined to be potential serological biomarkers for liver cirrhosis and HCC, respectively. Thus, median-centered/IgM-TIS assay normalization is a useful approach for analyzing antibody array data and evaluating serological biomarkers for the diagnosis of liver disease or cancers.     

Empirical likelihood-based inferences for the area under the ROC curve with covariates

In the receiver operating characteristic (ROC) analysis,the area under the ROC curve (AUC) is a popular summary index of discriminatory accuracy of a diagnostic test.Incorporating covariates into ROC analysis can improve the diagnostic accuracy of the test.Regression model for the AUC is a tool to evaluate the effects of the covariates on the diagnostic accuracy.In this paper,empirical likelihood (EL) method is proposed for the AUC regression model.For the regression parameter vector,it can be shown that the asymptotic distribution of its EL ratio statistic is a weighted sum of independent chi-square distributions.Confidence regions are constructed for the parameter vector based on the newly developed empirical likelihood theorem,as well as for the covariate-specific AUC.Simulation studies were conducted to compare the relative performance of the proposed EL-based methods with the existing method in AUC regression.Finally,the proposed methods are illustrated with a real data set.     

Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI.     

“Polymeromics”: Mass spectrometry based strategies in polymer science toward complete sequencing approaches: A review

Mass spectrometry (MS) is the most versatile and comprehensive method in “OMICS” sciences (i.e. in proteomics, genomics, metabolomics and lipidomics). The applications of MS and tandem MS (MS/MS or MSⁿ) provide sequence information of the full complement of biological samples in order to understand the importance of the sequences on their precise and specific functions. Nowadays, the control of polymer sequences and their accurate characterization is one of the significant challenges of current polymer science. Therefore, a similar approach can be very beneficial for characterizing and understanding the complex structures of synthetic macromolecules. MS-based strategies allow a relatively precise examination of polymeric structures (e.g. their molar mass distributions, monomer units, side chain substituents, end-group functionalities, and copolymer compositions). Moreover, tandem MS offer accurate structural information from intricate macromolecular structures; however, it produces vast amount of data to interpret. In “OMICS” sciences, the software application to interpret the obtained data has developed satisfyingly (e.g. in proteomics), because it is not possible to handle the amount of data acquired via (tandem) MS studies on the biological samples manually. It can be expected that special software tools will improve the interpretation of (tandem) MS output from the investigations of synthetic polymers as well. Eventually, the MS/MS field will also open up for polymer scientists who are not MS-specialists. In this review, we dissect the overall framework of the MS and MS/MS analysis of synthetic polymers into its key components. We discuss the fundamentals of polymer analyses as well as recent advances in the areas of tandem mass spectrometry, software developments, and the overall future perspectives on the way to polymer sequencing, one of the last Holy Grail in polymer science.     

Diagnostic value of elastosonography for thyroid microcarcinoma

Objective

To assess the diagnostic value of elastosonography for thyroid microcarcinoma (TMC), particularly with regard to elasticity score (ES) and strain ratio (SR).

Methods

Conventional ultrasound and elastosonography were performed for 487 thyroid micronodules before surgery. We set the histology as the reference standard. The ES and SR values, as well as their diagnostic threshold and efficiency, were compared and analyzed by the receiver-operating characteristic (ROC) curve. Additional comparisons between TMC patients with and without extracapsular extension were also performed.

Results

Statistically significant differences (P < 0.05) in both ES and SR values were detected among the TMC and benign groups. The area under the ROC curve of SR was significantly greater than that of ES (0.956 and 0.844, respectively; P < 0.05). Using ES ? 3 and SR ? 3.65 as diagnostic threshold values, the diagnostic sensitivity, specificity, and accuracy of ES for differentiating benign and malignant nodules were 79.9%, 72.3%, and 80.5%, respectively, whereas those of SR were 86.6%, 85.3%, and 89.4%, respectively. The maximum diameter, microcalcification status, aspect ratio, bilateral cervical lymph node metastasis, and SR values of nodules with extracapsular extension (A1 subgroup) were greater than those of nodules without extracapsular extension (A2 subgroup).

Conclusions

Elasticity imaging technology not only can help differentiate between benign and malignant thyroid micronodules but also allow SR values to provide accurate and objective information on tissue hardness and to predict TMC extracapsular extension or even bilateral cervical lymph node metastasis.