Numerous biomolecules possess α-D-glucosamine as structural component. However, chemical glycosylations aimed at this backbone are usually not easily attained without generating the unwanted β-isomer. We report herein a versatile approach in affording full α-stereoselectivity built upon a carefully selected set of orthogonal protecting groups on a D-glucosaminyl donor. The excellent stereoselectivity provided by the protecting group combination was found independent of leaving groups and activators. With the trichloroacetimidate as the optimum donor leaving group, core skeletons of glycosylphosphatidyl inositol anchors, heparosan, heparan sulfate, and heparin were efficiently assembled. The orthogonal protecting groups were successfully manipulated to further carry out the total syntheses of heparosan tri- and pentasaccharides and heparin di-, tetra-, hexa-, and octasaccharide analogues. Using the heparin analogues, heparin-binding hemagglutinin, a virulence factor of Mycobacterium tuberculosis, was found to bind at least six sugar units with the interaction notably being entropically driven.
The ordered tin disulfide (SnS2) nanowire arrays were first fabricated by sulfurizing the Sn nanowires, which are embedded in the nanochannels of anodic aluminum oxide (AAO) template. SnS2nanowire arrays are highly ordered and highly dense. X-ray diffraction (XRD) and corresponding selected area electron diffraction (SAED) patterns demonstrate the SnS2nanowire is hexagonal polycrystalline. The study of UV/Visible/NIR absorption shows the SnS2nanowire is a wide-band semiconductor with three band gap energies (3.3, 4.4, and 5.8 eV).
In this paper, we investigated the feasibility of using surface enhanced Raman spectroscopy (SERS) and multivariate analysis method to discriminate liver cancer and nasopharyngeal cancer from healthy volunteers. SERS measurements were performed on serum protein samples from 104 liver cancer patients, 100 nasopharyngeal cancer patients, and 95 healthy volunteers. Two dimensionality reduction methods, principal component analysis (PCA) and partial least square (PLS) were compared, and the results indicated that the performance of PLS is superior to that of PCA. When the number of components was compressed to 3 by PLS, support vector machine (SVM) with a Gaussian radial basis function (RBF) was employed to classify various cancers simultaneously. Based on the PLS-SVM algorithm, high diagnostic accuracies of 95.09% and 90.67% were achieved from the training set and the unknown testing set, respectively. The results of this exploratory work demonstrate that serum protein SERS technology combined with PLS-SVM diagnostic algorithm has great potential for the noninvasive screening of cancer.
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