Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75 percent for high-grade neuroglial tumors, 55 percent for meningiomas and 56 percent for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98 percent, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Peptidolytic microbial community of methanogenic reactors from two modified UASBs of brewery industries

We studied the peptide-degrading anaerobic communities of methanogenic reactors from two mesophilic full-scale modified upflow anaerobic sludge blanket (UASB) reactors treating brewery wastewater in Colombia. Most probable number (MPN) counts varied between 7.1 x 10(8) and 6.6 x 10(9) bacteria/g volatile suspended solids VSS (Methanogenic Reactor 1) and 7.2 x 10(6) and 6.4 x 10(7) bacteria/g (VSS) (Methanogenic Reactor 2). Metabolites detected in the highest positive MPN dilutions in both reactors were mostly acetate, propionate, isovalerate and, in some cases, negligible concentrations of butyrate. Using the highest positive dilutions of MPN counts, 50 dominant strains were isolated from both reactors, and 12 strains were selected for sequencing their 16S rRNA gene based on their phenotypic characteristics. The small-subunit rRNA gene sequences indicated that these strains were affiliated to the families Propionibacteriaceae, Clostridiaceae and Syntrophomonadaceae in the low G + C gram-positive group and Desulfovibrio spp. in the class d-Proteobacteria. The main metabolites detected in the highest positive dilutions of MPN and the presence of Syntrophomonadaceae indicate the effect of the syntrophic associations on the bioconversion of these substrates in methanogenic reactors. Additionally, the potential utilization of external electron acceptors for the complete degradation of amino acids by Clostridium strains confirms the relevance of these acceptors in the transformation of peptides and amino acids in these systems.