On the relevance of glycolysis process on brain gliomas.

The proposed analysis considers aspects of both statistical and biological validation of the glycolysis effect on brain gliomas, at both genomic and metabolic level. In particular, two independent datasets are analyzed in parallel, one engaging genomic (Microarray Expression) data and the other metabolomic (Magnetic Resonance Spectroscopy Imaging) data. The aim of this study is twofold. First to show that, apart from the already studied genes (markers), other genes such as those involved in the human cell glycolysis significantly contribute in gliomas discrimination. Second, to demonstrate how the glycolysis process can open new ways towards the design of patient-specific therapeutic protocols. The results of our analysis demonstrate that the combination of genes participating in the glycolytic process (ALDOA, ALDOC, ENO2, GAPDH, HK2, LDHA, LDHB, MDH1, PDHB, PFKM, PGI, PGK1, PGM1 and PKLR) with the already known tumor suppressors (PTEN, Rb, TP53), oncogenes (CDK4, EGFR, PDGF) and HIF-1, enhance the discrimination of low versus high-grade gliomas providing high prediction ability in a cross-validated framework. Following these results and supported by the biological effect of glycolytic genes on cancer cells, we address the study of glycolysis for the development of new treatment protocols.

Statistical methods for improving verification of claims of origin for Italian wines based on stable isotope ratios.

Wine derives its economic value to a large extent from geographical origin, which has a significant impact on the quality of the wine. According to the food legislation, wines can be without geographical origin (table wine) and wines with origin. Wines with origin must have characteristics which are essential due to its region of production and must be produced, processed and prepared, exclusively within that region. The development of fast and reliable analytical methods for the assessment of claims of origin is very important. The current official method is based on the measurement of stable isotope ratios of water and alcohol in wine, which are influenced by climatic factors. The results in this paper are based on 5220 Italian wine samples collected in the period 2000-2010. We evaluate the univariate approach underlying the official method to assess claims of origin and propose several new methods to get better geographical discrimination between samples. It is shown that multivariate methods are superior to univariate approaches in that they show increased sensitivity and specificity. In cases where data are non-normally distributed, an approach based on mixture modelling provides additional improvements.

Opening the kernel of kernel partial least squares and support vector machines.

Kernel partial least squares (KPLS) and support vector regression (SVR) have become popular techniques for regression of complex non-linear data sets. The modeling is performed by mapping the data in a higher dimensional feature space through the kernel transformation. The disadvantage of such a transformation is, however, that information about the contribution of the original variables in the regression is lost. In this paper we introduce a method which can retrieve and visualize the contribution of the variables to the regression model and the way the variables contribute to the regression of complex data sets. The method is based on the visualization of trajectories using so-called pseudo samples representing the original variables in the data. We test and illustrate the proposed method to several synthetic and real benchmark data sets. The results show that for linear and non-linear regression models the important variables were identified with corresponding linear or non-linear trajectories. The results were verified by comparing with ordinary PLS regression and by selecting those variables which were indicated as important and rebuilding a model with only those variables.

Discrimination between metastasis and glioblastoma multiforme based on morphometric analysis of MR images.

BACKGROUND AND PURPOSE: Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs. MATERIALS AND METHODS: The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called "clusters") represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure. RESULTS: A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases. CONCLUSIONS: A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.

Revealing the metabolic profile of brain tumors for diagnosis purposes.

The metabolic behavior of complex brain tumors, like Gliomas and Meningiomas, with respect to their type and grade was investigated in this paper. Towards this direction the smallest set of the most representative metabolic markers for each brain tumor type was identified, using ratios of peak areas of well established metabolites, from (1)H-MRSI (Proton Magnetic Resonance Spectroscopy Imaging) data of 24 patients and 4 healthy volunteers. A feature selection method that embeds Fisher's filter criterion into a wrapper selection scheme was applied; Support Vector Machine (SVM) and Least Squares-SVM (LS-SVM) classifiers were used to evaluate the ratio markers classification significance. The area under the Receiver Operating Characteristic curve (AUROC) was adopted to evaluate the classification significance. It is found that the NAA/CHO, CHO/S, MI/S ratios can be used to discriminate Gliomas and Meningiomas from Healthy tissue with AUROC greater than 0.98. Ratios CHO/S, CRE/S, MI/S, LAC/CRE, ALA/CRE, ALA/S and LIPS/CRE can identify type and grade differences in Gliomas giving AUROC greater than 0.98 apart from the scheme of Gliomas grade II vs grade III where 0.84 was recorded due to high heterogeneity. Finally NAA/CRE, NAA/S, CHO/S, MI/S and ALA/S manage to discriminate Gliomas from Meningiomas providing AUROC exceeding 0.90.

Effect on the partial least-squares prediction of yarn properties combining raman and infrared measurements and applying wavelength selection.

The combination of Raman and infrared spectroscopy on the one hand and wavelength selection on the other hand is used to improve the partial least-squares (PLS) prediction of seven selected yarn properties. These properties are important for on-line quality control during production. From 71 yarn samples, the Raman and infrared spectra are measured and reference methods are used to determine the selected properties. Making separate PLS models for all yarn properties using the Raman and infrared spectra, prior to wavelength selection, reveals that Raman spectroscopy outperforms infrared spectroscopy. If wavelength selection is applied, the PLS prediction error decreases and the correlation coefficient increases for all properties. However, a substantial wavelength selection effect is present for the infrared spectra compared to the Raman spectra. For the infrared spectra, wavelength selection results in PLS prediction errors comparable with the prediction performance of the Raman spectra prior to wavelength selection. Concatenating the Raman and infrared spectra does not enhance the PLS prediction performance, not even after wavelength selection. It is concluded that an infrared spectrometer, combined with a wavelength selection procedure, can be used if no (suitable) Raman instrument is available.

Pharmacokinetics of 24,25-dihydroxyvitamin D3 in humans.

Pharmacokinetic properties of pharmacological doses of 24,25-dihydroxyvitamin-D3 [24,25(OH)2D3] were determined in healthy volunteers. Four male subjects received 25 micrograms of 24,25(OH)2D3 as an intravenous bolus injection. Plasma concentrations of 24,25(OH)2D3, 25-hydroxyvitamin D and 1,25-dihydroxy-vitamin D were monitored during 14 days. In addition, serum ionized calcium, total calcium, inorganic phosphate, albumin, creatinine and intact hPTH(1-84) were measured during 14 days. The concentration-time curve of 24,25(OH)2D3 could be described by a two-exponential curve with half-lives of 3.0 +/- 0.9 hrs and 8.2 +/- 2.9 days (mean +/- SD). The volume of distribution was 0.19 +/- 0.02 liters/kg. None of the mentioned biochemical parameters, except serum 24,25(OH)2D3, changed markedly. In 18 subjects suffering from primary hyperparathyroidism, taking 25 micrograms of 24,25(OH)2D3 daily during three months, an average plateau level of 39 +/- 12 nmol/l of serum was observed. Bioavailability as estimated from this plateau level was approximately 70%.

Determination of ethylenediamineplatinum(II) malonate in infusion fluids, human plasma and urine by high-performance liquid chromatography.

A selective and convenient high-performance liquid chromatographic assay was developed for ethylenediamineplatinum(II) malonate (JM-40) in plasma ultrafiltrate and urine. A mu Porasil silica column (30 cm) was used with acetonitrile-water (90:10, v/v) as the mobile phase and the elution of compounds was monitored by ultraviolet absorbance at 214 nm. A linear dynamic range of at least three decades (1-1000 micrograms/ml) was achieved. The detection limit in plasma ultrafiltrate was 0.35 micrograms/ml. The stability of JM-40 was determined in 0.9% sodium chloride, 5% glucose, plasma ultrafiltrate and urine. More stable drug solutions were obtained with 5% glucose than with 0.9% sodium chloride. JM-40 was also determined in plasma ultrafiltrate and urine samples of one patient receiving short-term infusions of the drug. In plasma ultrafiltrate unmetabolized JM-40 was detected during the first 5 h after administration and had a half-life of 21.3 +/- 1.6 min. The parent drug was excreted in the urine in rapidly decreasing amounts. Eighteen per cent of the dose was recovered as unmetabolized drug during the first 6 h.