In vitro and in vivo assessment of glucose cross-linked gelatin/zein nanofibrous scaffolds for cranial bone defects regeneration.

The purpose of this study was to evaluate the glucose cross-linked gelatin/zein scaffolds for bone regeneration in vitro and in vivo. The nanofibrous scaffolds exhibited fast mineralization in the concentrated simulated body fluid with the deposited octacalcium phosphate and dicalcium phosphate dehydrate. The nanofibrous scaffolds exhibited no cytotoxic effect on MC3T3e1 cells in a CCK-8 test. Additionally, scanning electron microscope and confocal laser scanning microscopy images revealed that all the scaffolds were biocompatible and showed excellent support for MC3T3e1 cells. In the osteogenesis characterizations, Alizarin Red staining experiments indicated the improved calcium deposits on the cross-linked scaffolds, while the alkaline phosphatase activity showed no difference. Furthermore, the in vivo cranial bone regeneration results suggested that the cross-linked gelatin/zein scaffolds presented a strong positive effect on the cranial bone regeneration with the increased new bone volume and connective tissue formation, but the incorporation of zein in the gelatin scaffolds did not favor the bone regeneration. Moreover, the cross-linked gelatin scaffold retarded the bone resorption as indicated by the higher levels of IFN-γ and lower levels of IL-6, which restricted the differentiation of osteoclasts.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls.

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.