Frullano L.,Case Center for Imaging Research |
Wang C.,Case Center for Imaging Research |
Miller R.H.,Case Western Reserve University |
Wang Y.,Case Center for Imaging Research
Journal of the American Chemical Society | Year: 2011
Myelination is one of the most fundamental biological processes in the development of vertebrate nervous systems. Abnormal or disrupted myelination occurs in many acquired or inherited neurodegenerative diseases, including multiple sclerosis (MS) and various leukodystrophies. To date, magnetic resonance imaging (MRI) has been the primary tool for diagnosing and monitoring the progression of MS and related diseases; however, any change in signal intensity of conventional MRI reflects a change only in tissue water content, which is a nonspecific measure of the overall changes in macroscopic tissue injury. Thus, the use of MRI as a primary measure of disease activity was shown to be disassociated from the clinical outcome due to the lack of specificity for myelination. In order to increase the MRI specificity for myelin pathologies, we designed and synthesized the first Gd-based T 1 MR contrast agent (MIC) that binds to myelin with high specificity. In this Communication, we demonstrate that MIC localizes in brain regions in proportion to the extent of myelination. In addition, MIC possesses promising MR contrast properties, which allow for direct detection of myelin distribution through T 1 mapping in the mouse brain. © 2011 American Chemical Society.
Ni T.,Changzhou University |
Ni T.,Jiangnan University |
Gu X.,Changzhou University |
Gu X.,Jiangnan University |
And 6 more authors.
Journal of Information Science and Engineering | Year: 2015
Manifold regularization, which learns from a limited number of labeled samples and a large number of unlabeled samples, is a powerful semi-supervised classifier with a solid theoretical foundation. However, manifold regularization has the tendency to misclassify data near the boundaries of different classes during the classification process. In this paper, we propose a novel classification method called locality preserving semi-supervised support vector machine (LPSSVM) with an extended manifold regularization framework based on within-class locality preserving scatter. LPSSVM is good at exploring the underlying discriminative information as well as the local geometry of the samples as much as possible rather than merely relying on the smoothness information regarding manifold regularization. Meanwhile, benefiting from the geodesic distance metric, LPSSVM can more effectively reflect the true local geometry of data instances in the manifold space, which further strengths its accuracy in reality. The extensive comparisons with respect to LPSSVM and several state-of-the-art approaches were carried out on both artificial and real-word data sets. These experimental studies demonstrate the advantages as well as the superiority of our proposed method.
Perera V.S.,Kent State University |
Hao J.,Case Center for Imaging Research |
Hao J.,Case Western Reserve University |
Gao M.,Kent State University |
And 6 more authors.
Inorganic Chemistry | Year: 2011
An aqueous synthetic procedure for preparing nanoparticles of the novel potassium bismuth ferrocyanide coordination polymer KBi(H 2O) 2[Fe(CN) 6]·H 2O is reported. The crystal structure of this coordination polymer is determined through X-ray powder diffraction using the bulk materials. The stability, cytotoxicity, and potential use of such nanoparticles coated with PVP as a CT contrast agent are investigated. © 2011 American Chemical Society.