Composition of Ni2+ cation solvation shell in NiCl2–methanol solution by multinuclear NMR

1H-, 2H- and 13C-NMR spectra have been used to test the Ni2+ solvation shell composition in the 1.1 molal methanol solution of NiCl2. It has been confirmed that Cl− anion takes part in the nearest environment of Ni2+ cation at all the temperatures investigated. Using 2H-NMR allowed us to detect for the first time OD-signal of methanol in the primary solvation shell of Ni2+ cation. Both 2H- and 13C-NMR spectra show that the composition of the cation solvation shell becomes more complicated at temperatures lower than 220 K

1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death

Peer reviewe

Unexpected Temperature Behavior of Polyethylene Glycol Spacers in Copolymer Dendrimers in Chloroform

We have studied copolymer dendrimer structure: carbosilane dendrimers with terminal phenylbenzoatemesogenic groups attached by poly(ethylene) glycol (PEG) spacers. In this system PEG spacers areadditional tuning to usual copolymer structure: dendrimer with terminal mesogenic groups. Thedendrimer macromolecules were investigated in a dilute chloroform solution by 1H NMR methods(spectra and relaxations). It was found that the PEG layer in G = 5 generations dendrimer is “frozen”at high temperatures (above 260 K), but it unexpectedly becomes “unfrozen” at temperatures below250 K (i.e., melting when cooling). The transition between these two states occurs within a smalltemperature range (~10 K). Such a behavior is not observed for smaller dendrimer generations (G = 1and 3). This effect is likely related to the low critical solution temperature (LCST) of PEG and is caused bydendrimer conformations, in which the PEG group concentration in the layer increases with growing G.We suppose that the unusual behavior of PEG fragments in dendrimers will be interesting for practicalapplications such as nanocontainers or nanoreactors.</p

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

<p>Abstract</p> <p>Background</p> <p>A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by ¹H NMR spectroscopy of serum.</p> <p>Results</p> <p>A Bayesian methodology, with a biochemical motivation, is presented for a real ¹H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the ¹H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the ¹H NMR spectra.</p> <p>Conclusion</p> <p>The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.</p