Alzheimer's Disease and Risk of Hip Fracture: A Meta-Analysis Study

Background. Alzheimer's disease (AD) is the most common cause of dementia in the elderly population. Growing evidence supports that AD patients are at high risk for hip fracture, but the issue remains questionable. The purpose of the present study is to perform a meta-analysis to explore the association between AD and risk of hip fracture. Considering that bone mineral density (BMD) acts as a strong predictor of bone fracture, we also studied the hip BMD in AD patients. Methods. We searched all publications in Medline, SciVerse Scopus, and Cochrane Library published up to January 2012 about the association between AD and hip fracture or hip BMD. Results. There are 9 studies included in the meta-analysis. The results indicate that AD patients are at higher risk for hip fracture (OR and 95% CI fixed: ES = 2.58, 95% CI = [2.03, 3.14]; dichotomous data: summary OR = 1.80, 95% CI = [1.54, 2.11]) than healthy controls. Further meta-analysis showed that AD patients have a lower hip BMD (summary SMD = −1.12, 95% CI = [−1.34, −0.90]) than healthy controls. Conclusions. It was found that in comparison with healthy controls AD patients are at higher risk for hip fracture and have lower hip BMD

5,5′,5′′-Triphenyl-2,2′,2′′-[2,4,6-tri­methyl­benzene-1,3,5-triyltris(methyl­idene­sulfanedi­yl)]tris­(1,3,4-oxadiazole)

In the title compound, C36H30N6O3S3, the phenyl rings are twisted from the attached oxadiazole rings in the three arms by 1.5(2), 2.4 (2) and 25.7 (2)°. The crystal packing exhibits weak inter­molecular C—H⋯N inter­actions

5,5′-Diphenyl-2,2′-[butane-1,4-diylbis(sulfanedi­yl)]bis­(1,3,4-oxadiazole)

The complete mol­ecule of the title compound, C20H18N4O2S2, is generated by crystallographic inversion symmetry. The benzene ring is almost coplanar with the oxadiazole ring [dihedral angle = 7.2 (2)°]

A Label-Free Platform for Identification of Exosomes from Different Sources.

Exosomes contain cell- and cell-state-specific cargos of proteins, lipids, and nucleic acids and play significant roles in cell signaling and cell-cell communication. Current research into exosome-based biomarkers has relied largely on analyzing candidate biomarkers, i.e., specific proteins or nucleic acids. However, this approach may miss important biomarkers that are yet to be identified. Alternative approaches are to analyze the entire exosome system, either by "omics" methods or by techniques that provide "fingerprints" of the system without identifying each individual biomolecule component. Here, we describe a platform of the latter type, which is based on surface-enhanced Raman spectroscopy (SERS) in combination with multivariate analysis, and demonstrate the utility of this platform for analyzing exosomes derived from different biological sources. First, we examined whether this analysis could use exosomes isolated from fetal bovine serum using a simple, commercially available isolation kit or necessitates the higher purity achieved by the "gold standard" ultracentrifugation/filtration procedure. Our data demonstrate that the latter method is required for this type of analysis. Having established this requirement, we rigorously analyzed the Raman spectral signature of individual exosomes using a unique, hybrid SERS substrate made of a graphene-covered Au surface containing a quasi-periodic array of pyramids. To examine the source of the Raman signal, we used Raman mapping of low and high spatial resolution combined with morphological identification of exosomes by scanning electron microscopy. Both approaches suggested that the spectra were collected from single exosomes. Finally, we demonstrate for the first time that our platform can distinguish among exosomes from different biological sources based on their Raman signature, a promising approach for developing exosome-based fingerprinting. Our study serves as a solid technological foundation for future exploration of the roles of exosomes in various biological processes and their use as biomarkers for disease diagnosis and treatment monitoring