Resolution of enantiomers by HPLC on cellulose derivatives

Summary Various polysaccharide derivatives, particularly cellulose derivatives, were synthesized and used as chiral stationary phases for optical resolution by HPLC after being adsorbed on macroporous silica gel. Cellulose triacetate (CTA-II), which was synthesized under homogeneous conditions, showed a chiral recognition ability for many racemates. Other cellulose derivatives such as cellulose tribenzoate (OB), cellulose-trisphenylcarbamate (OC), cellulose tribenzyl ether (OE), and cellulose tricinnamate (OK) also showed unique chiral recognition. Among other polysaccharide derivatives, curdlan triacetate was also exhibited an effective chiral recognition. Presented at the 15th International Symposium on Chromatography, Nürnberg, October 1984     

Dependence of chiral recognition on the degree of substitution of cellulose benzoate

A series of cellulose benzoates with different degrees of substitution (DS) were prepared by aminolysis under homogeneous conditions. Using them as a stationary phase, the dependence of the chromatographic chiral recognition of cellulose benzoate (CB) on its DS was studied under normal phase conditions. For most enantiomeric pairs, chiral recognition decreased along with the decrease in DS. However, some enantiomeric pairs were resolved only by CBs carrying unsubstituted hydroxyl groups. Many of the compounds were quite strongly retained by cellulose benzoate with a DS of 2.8. A study of the retention behaviors of simple achiral compounds led us to the conclusion that the change in the retention behaviors by introducing free hydroxyl groups into tribenzoate is not caused by hydrogen bonding including the hydroxyl groups, but by a specific unidentified change.     

Genetic and expression analysis of SNPs in the human deoxyribonuclease II: SNPs in the promoter region reduce its in vivo activity through decreased promoter activity

Five SNPs in the human DNase II gene have been reported to be associated with rheumatoid arthritis (RA). Genotype and haplotype analysis of 14 SNPs, nine SNPs of which reported in the NCBI dbSNP database in addition to these five SNPs, was performed in healthy subjects. The enzymatic activities of the amino acid substituted DNase II corresponding to each SNP and serum DNase II in healthy Japanese, and promoter activities derived from each haplotype of the RA‐related SNPs were measured. Significant correlations between genotype in each RA‐related SNP and enzymatic activity levels were found; alleles associated with RA exhibited a reduction in serum DNase II activity. Furthermore, the promoter activities of each reporter construct corresponding to predominant haplotypes in three SNPs in the promoter region of the gene exhibited significant correlation with levels of serum DNase II activity. These findings indicate these three SNPs could alter the promoter activity of DNASE2, leading to a decline in DNase II activity in the serum through gene expression. Since the three SNPs in the promoter region of the DNase II gene could affect in vivo DNase II activity through reduction of the promoter activity, it is feasible to identify these SNPs susceptible to RA.     

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.     

Efficient conversion of thymine glycol into the formamide lesion in oligonucleotides

Oxidation of (5R,6S)-5,6-dihydro-5,6-dihydroxythymidine (thymidine glycol) with sodium periodate efficiently produced N-(2-deoxy-β-d-erythro-pentofuranosyl)formamide, a hydroxyl radical-induced decomposition product of pyrimidine bases in DNA, and this method was successfully applied to the conversion of thymine glycol in oligonucleotides into the formamide lesion.