Protective effect of ethanolic extract of Echinacea purpurea contained nanoparticles on meniscal/ligamentous injury induced osteoarthritis in obese male rats.

Osteoarthritis (OA) is a chronic degenerative joint disease associated with age, mechanical stress, and obesity. Echinacea purpurea is a medicinal plant that shows good anti-inflammatory, antioxidant, and immunomodulatory activities. In this study, Echinacea purpurea ethanol extract nanoparticles (Nano-EE) were prepared by encapsulating Echinacea purpurea ethanol extract (EE) in chitosan-silica nanoparticles. Obesity (OB) in Sprague-Dawley (SD) rats was induced by fed 40% high-fat diet and then anterior cruciate ligament and meniscus injury were performed to induce OA. The rats got different doses of samples by oral gavage. The encapsulation efficiency and loading capacity of Nano-EE were 69.1% and 36.1%, respectively. The average size, polydispersity index (PDI), and zeta potential (ZP) of the Nano-EE were 145 ± 11 nm, 0.24 ± 0.01, - 4.57 ± 0.44 mV, respectively. Furthermore, electron microscopic images showed that the particles were spherical and were slightly agglomerated. Moreover, it showed that the leptin content, expression of MMPs, cytokines level, NF-κB level, and iNOS production were decreased whereas collagen II expression was increased after treatment. Besides, Nano-EE ameliorated the pain caused by OA and reduced the proteoglycan loss in cartilage. These results indicated that encapsulated EE (Nano-EE) can ameliorate OA with a low dosage and are more effective than unencapsulated EE.

Automated tongue diagnosis on the smartphone and its applications.

Tongue features are important objective basis for clinical diagnosis and treatment in both western medicine and Chinese medicine. The need for continuous monitoring of health conditions inspires us to develop an automatic tongue diagnosis system based on built-in sensors of smartphones. However, tongue images taken by smartphone are quite different in color due to various lighting conditions, and it consequently affects the diagnosis especially when we use the appearance of tongue fur to infer health conditions. In this paper, we captured paired tongue images with and without flash, and the color difference between the paired images is used to estimate the lighting condition based on the Support Vector Machine (SVM). The color correction matrices for three kinds of common lights (i.e., fluorescent, halogen and incandescent) are pre-trained by using a ColorChecker-based method, and the corresponding pre-trained matrix for the estimated lighting is then applied to eliminate the effect of color distortion. We further use tongue fur detection as an example to discuss the effect of different model parameters and ColorCheckers for training the tongue color correction matrix under different lighting conditions. Finally, in order to demonstrate the potential use of our proposed system, we recruited 246 patients over a period of 2.5 years from a local hospital in Taiwan and examined the correlations between the captured tongue features and alanine aminotransferase (ALT)/aspartate aminotransferase (AST), which are important bio-markers for liver diseases. We found that some tongue features have strong correlation with AST or ALT, which suggests the possible use of these tongue features captured on a smartphone to provide an early warning of liver diseases.