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Objective: To explore the histological structure of the deciduous teeth and the tooth germs of Tibetan miniature pigs for studies of dental tissue diseases and tooth regeneration., Methods: The structure of the deciduous teeth of Tibetan miniature pigs was observed by X-ray. The ultrastructure of the enamel and dentin of deciduous teeth was characterized by scanning electron microscopy. The jaws and teeth were three-dimensionally reconstructed using Mimics software based on Micro-CT scanning of the deciduous teeth. Image J software was used to calculate the gray value and the mineralization density of the deciduous teeth. Hisotological structure of the tooth germ and the pulp tissue of Tibetan miniature pigs was observed using HE staining., Results: The deciduous teeth of Tibetan miniature pigs were composed of enamel, dentin and medullary pulp tissue. The permanent tooth germ were formed during the deciduous dentition. The enamel and dentin ultrastructure of deciduous teeth were consistent with that of human deciduous teeth. The enamel and dentin mineralization densities were 2.47±0.09 g/cm 3 and 1.72±0.07 g/cm 3 , respectively. The pathological structures of tooth germ and pulp tissue were similar to those of human teeth, and the pulp tissue of the deciduous teeth was in an undifferentiated state., Conclusions: The deciduous teeth of Tibetan miniature pig have similar anatomy, ultrastructure and histopathological structure to human teeth and can serve as a good animal model for studying human dental tissue diseases and the mechanisms of tooth regeneration.

Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression data from a malaria vaccine trial by big-data-based edge biomarkers from module network rewiring-analysis. The illustrative results show that the identified module biomarkers can accurately distinguish vaccines with or without protection and outperformed previous reported gene signatures in terms of effectiveness and efficiency., (© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.)

A series of phosphorescent terpyridyl platinum(II) complexes with ancillary biphenylacetylide ligands, namely, [(R(3)tpy)PtC≡C(biphenyl)]X (R=tBu, H, or Et(2)N; tpy=2,2';6',2''-terpyridyl; X is an anion) were synthesized and structurally characterized by various spectroscopic techniques and X-ray diffraction methods. Despite a lack of long alkyl chain(s) or hydrogen-bonding motif(s), complexes [(tpy)PtC≡C(biphenyl)]Cl and [(tBu(3)tpy)PtC≡C(biphenyl)]X (X=Cl, ClO(4), PF(6), or BF(4)) were found to gelate water and organic solvents, respectively. The self-aggregation of these complexes in solutions and the resulting gels were investigated with variable-temperature (VT) (1)H NMR spectroscopy, polarized optical microscopy, and absorption/emission spectroscopy. SEM micrographs on dry gels revealed entangled nanofibers with diameters of 20-40 nm and lengths of tens of micrometers. Powder X-ray diffraction (PXRD) study revealed various degrees of crystallinity of these fibrillar nanostructures. The substituents on both the terpyridyl and acetylide ligands and counterion of these complexes play a profound but concerted role in tuning the intermolecular metal···metal and/or π-π interactions, and hence the gelation properties., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)