Complete 1H, 13C, 19F and11B NMR spectral data for 28 potassium organotrifluoroborates are described. The resonance for the carbon bearing the boron atom is described for most of the studied compounds. A modified 11B NMR pulse sequence was used and better resolution was observed allowing the observation of 11B–19F coupling constants for some of the studied compounds.

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Metabonomics based on nuclear magnetic resonance (NMR) can reveal the profile of endogenous metabolites of low molecular weight in biofluids related to disease. The profile is identified a 'metabolic fingerprint' like from the pathological process, why this metabonomics has been used as a diagnostic method. The aim of the present study was to apply metabonomics to identify patients infected with the hepatitis C virus (HCV) through an analysis of ¹H NMR spectra of urine samples associated with multivariate statistical methods. A pilot study was carried out for the diagnostic test evaluation, involving two groups: (i) 34 patients positive for anti-HCV and HCV-RNA and negative for anti-HBc (disease group); and (ii) 32 individuals positive for anti-HBc and negative for HBsAg and anti-HCV. The urine samples were analyzed through ¹H NMR, applying principal component analysis and discriminant analysis for classification. The metabonomics model was capable of identifying 32 of the 34 patients in the disease group as positive and 31 of the 32 individuals in the control group as negative, demonstrating 94% sensitivity and specificity of 97% as well as positive and negative predictive values of 97% and 94%, respectively, and 95% accuracy (P < 0.001). In conclusion, the metabonomics model based on ¹H NMR spectra of urine samples in this preliminary study discriminated patients with HCV infection with high sensitivity and specificity, thereby demonstrating this model to be a potential tool for use in medical practice in the near future.

Research in recent years has demonstrated that the Trypanosoma cruzi cysteine protease cruzain (TCC) is a valid chemotherapeutic target. Herein we describe a small library of aryl-4-oxothiazolylhydrazones that have been tested in assays against T. cruzi cell cultures. The docking studies carried out suggest that these compounds are potential ligands for the TCC enzyme. The most promising compound of this series, N-(4-oxo-5-ethyl-2'-thiazolin-2-yl)-N'-phenylthio-(Z)-ethylidenehydrazone (6 f), was shown to be very active at non-cytotoxic concentrations in in vitro assays with mammalian cells and has a potency comparable with reference drugs such as nifurtimox (Nfx) and benznidazole (Bdz).

This is a report on how 1 H NMR-based metabonomics was employed to discriminate osteopenia from osteoporosis in postmenopausal women, identifying the main metabolites associated to the separation between the groups. The Assays were performed using seventy-eight samples, being twenty-eight healthy volunteers, twenty-six osteopenia patients and twenty-four osteoporosis patients. PCA, LDA, PLS-DA and OPLS-DA formalisms were used. PCA discriminated the samples from healthy volunteers from diseased patient samples. Osteopenia-osteoporosis discrimination was only obtained using Analysis Discriminants formalisms, as LDA, PLS-DA and OPLS-DA. The metabonomics model using LDA formalism presented 88.0% accuracy, 88.5% specificity and 88.0% sensitivity. Cross-Validation, however, presented some problems as the accuracy of modeling decreased. LOOCV resulted in 78.0% accuracy. The OPLS-DA based model was better: R2Y and Q2 values equal to 0.871 (p<0.001) and 0.415 (p<0.001). LDA and OPLS-DA indicated the important spectral regions for discrimination, making possible to assign the metabolites involved in the skeletal system homeostasis, as follows: VLDL, LDL, leucine, isoleucine, allantoin, taurine and unsaturated lipids. These results indicate that 1 H NMR-based metabonomics can be used as a diagnosis tool to discriminate osteoporosis from osteopenia using a single serum sample.