1.
Comprehensive analysis of serum metabolites in gestational diabetes mellitus by UPLC/Q-TOF-MS.
Liu, T, Li, J, Xu, F, Wang, M, Ding, S, Xu, H, Dong, F
Analytical and bioanalytical chemistry. 2016;(4):1125-35
Abstract
Gestational diabetes mellitus (GDM) refers to the first sign or onset of diabetes mellitus during pregnancy rather than progestation. In recent decades, more and more research has focused on the etiology and pathogenesis of GDM in order to further understand GDM progress and recovery. Using an advanced metabolomics platform based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS), we explored the changes in serum metabolites between women with GDM and healthy controls during and after pregnancy. Some significant differences were discovered using multivariate analysis including partial least-squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA). The dysregulated metabolites were further compared and verified in several databases to understand how these compounds might function as potential biomarkers. Analyses of the metabolic pathways associated with these potential biomarkers were subsequently explored. A total of 35 metabolites were identified, contributing to GDM progress to some extent. The identified biomarkers were involved in some important metabolic pathways including glycine, serine, and threonine metabolism; steroid hormone biosynthesis; tyrosine metabolism; glycerophospholipid metabolism; and fatty acid metabolism. The above mentioned metabolic pathways mainly participate in three major metabolic cycles in humans, including lipid metabolism, carbohydrate metabolism, and amino acid metabolism. In this pilot study, the valuable comprehensive analysis gave us further insight into the etiology and pathophysiology of GDM, which might benefit the feasibility of a rapid, accurate diagnosis and reasonable treatment as soon as possible but also prevent GDM and its related short- and long-term complications.
2.
Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis.
Palmer, GM, Zhu, C, Breslin, TM, Xu, F, Gilchrist, KW, Ramanujam, N
Applied optics. 2006;(5):1072-8
Abstract
The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.