Ziyuglycoside II attenuated OVX mice bone loss via inflammatory responses and regulation of gut microbiota and SCFAs.

BACKGROUND AND PURPOSE: Osteoporosis (OP) is a frequent clinical problem for the elderly. Traditional Chinese Medicine (TCM) has achieved beneficial results in the treatment of OP. Ziyuglycoside II (ZGS II) is a major active compound of Sanguisorba officinalis L. that has shown anti-inflammation and antioxidation properties, but little information concerning its anti-OP potential is available. Our research aims to investigate the mechanism of ZGS II in ameliorating bone loss by inflammatory responses and regulation of gut microbiota and short chain fatty acids (SCFAs) in ovariectomized (OVX) mice. METHODS: We predicted the mode of ZGS II action on OP through network pharmacology and molecular docking, and an OVX mouse model was employed to validate its anti-OP efficacy. Then we analyzed its impact on bone microstructure, the levels of inflammatory cytokines and pain mediators in serum, inflammation in colon, intestinal barrier, gut microbiota composition and SCFAs in feces. RESULTS: Network pharmacology identified 55 intersecting targets of ZGS II related to OP. Of these, we predicted IGF1 may be the core target, which was successfully docked with ZGS II and showed excellent binding ability. Our in vivo results showed that ZGS II alleviated bone loss in OVX mice, attenuated systemic inflammation, enhanced intestinal barrier, reduced the pain threshold, modulated the abundance of gut microbiota involving norank_f__Muribaculaceae and Dubosiella, and increased the content of acetic acid and propanoic acid in SCFAs. CONCLUSIONS: Our data indicated that ZGS II attenuated bone loss in OVX mice by relieving inflammation and regulating gut microbiota and SCFAs.

Thermo-Responsive Hydrogels Coupled with Photothermal Agents for Biomedical Applications.

Intelligent hydrogels are materials with abilities to change their chemical nature or physical structure in response to external stimuli showing promising potential in multitudinous applications. Especially, photo-thermo coupled responsive hydrogels that are prepared by encapsulating photothermal agents into thermo-responsive hydrogel matrix exhibit more attractive advantages in biomedical applications owing to their spatiotemporal control and precise therapy. This work summarizes the latest progress of the photo-thermo coupled responsive hydrogel in biomedical applications. Three major elements of the photo-thermo coupled responsive hydrogel, i.e., thermo-responsive hydrogel matrix, photothermal agents, and construction methods are introduced. Furthermore, the recent developments of these hydrogels for biomedical applications are described with some selected examples. Finally, the challenges and future perspectives for photo-thermo coupled responsive hydrogels are outlined.

Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach.

Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain's biological network. In some recently proposed multimodal SCCA methods, due to the limitations of penalty items, the pre-processed data needs to be further filtered to make the dimensions uniform, which may destroy the potential association of data in the same modal. In this research, in order to combine data between different modalities and to ensure that the chain relationship or graph network relationship within the same modality will not be destroyed, the original generalized fused lasso penalty was replaced with the fused pairwise group lasso (FGL) and the graph-guided pairwise group lasso (GGL) based on the method of joint sparse canonical correlation analysis (JSCCA). We used prior knowledge to construct a supervised bivariate learning model and use linear regression to select quantitative traits (QTs) of images that are strongly correlated with the Mini-mental State Examination (MMSE) scores. Compared with FGL-SCCA, the model we constructed obtained a higher gene-ROI correlation coefficient and identified more significant biomarkers, providing a theoretical basis for further understanding the complex pathology of neurodegenerative diseases.