Study on the Interactions Between Cisplatin and Cadherin by Fluorescence Spectrometry and Atomic Force Microscopy.

Cisplatin is an important platinum drug in cancer chemotherapy in clinical practice. It is well established that the main target of cisplatin is nuclear DNA. However, recent studies have demonstrated that platinum drugs may act on some important functional proteins in the human body. E-cadherin is a newly discovered glycoprotein that has been regarded as an important sign of the occurrence and development of some tumors. This study examines the interactions between cisplatin and E-cadherin by fluorescence spectrometry and atomic force microscopy (AFM). The fluorescence spectrometry results indicated that cisplatin can efficiently quench the fluorescence of E-cadherin. The calculated binding constant Kb was 3.20 × 106 (25 ℃), 1.36 × 106(31 ℃), and 8.22 × 105 L mol-1 (37 ℃). These results reveal that the fluorescence quenching effect of cisplatin on E-cadherin is static quenching. The obtained thermodynamic parameters ΔH < 0, ΔS < 0, and ΔG < 0, indicate that the binding of cisplatin on E-cadherin is a spontaneous process dominated by hydrogen bonds and Van der Waals forces. The AFM results revealed that E-cadherins are interlaced with each other to form a spherical-chain structure. The addition of cisplatin can significantly disrupt the interlaced structure of the E-cadherin molecules.

Lysimachia christinae polysaccharide attenuates diet-induced hyperlipidemia via modulating gut microbes-mediated FXR-FGF15 signaling pathway.

Polysaccharides are one of the most abundant and active components of Lysimachia christinae (L. christinae), which is widely adopted for attenuating abnormal cholesterol metabolism; however, its mechanism of action remains unclear. Therefore, we fed a natural polysaccharide (NP) purified from L. christinae to high-fat diet mice. These mice showed an altered gut microbiota and bile acid pool, which was characterized by significantly increased Lactobacillus murinus and unconjugated bile acids in the ileum. Oral administration of the NP reduced cholesterol and triglyceride levels and enhanced bile acid synthesis via cholesterol 7α-hydroxylase. Additionally, the effects of NP are microbiota-dependent, which was reconfirmed by fecal microbiota transplantation (FMT). Altered gut microbiota reshaped bile acid metabolism by modulating bile salt hydrolase (BSH) activity. Therefore, bsh genes were genetically engineered into Brevibacillus choshinensis, which was gavaged into mice to verify BSH function in vivo. Finally, adeno-associated-virus-2-mediated overexpression or inhibition of fibroblast growth factor 15 (FGF15) was used to explore the farnesoid X receptor-fibroblast growth factor 15 pathway in hyperlipidemic mice. We identified that the NP relieves hyperlipidemia by altering the gut microbiota, which is accompanied by the active conversion of cholesterol to bile acids.

Green Synthesis and Antimicrobial Study on Functionalized Chestnut-Shell-Extract Ag Nanoparticles.

The chestnut shell is usually discarded as agricultural waste and the random deposition of it can cause environmental problems. In this study, monodisperse crystalline Ag nanoparticles (AgNPs) were synthesized by a hydrothermal approach, in which the chestnut shell extract served as both reducing agent and stabilizer. The synthesized Ag nanoparticles were characterized by ultraviolet-visible (UV) spectrophotometry, transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) measurements. The TEM, XRD and XPS results revealed that the synthesized product was spherical Ag nanoparticles with a face-centered cubic crystal structure. The antimicrobial activity test indicated that the Ag nanoparticles modified by the chestnut shell extract had an obvious inhibitory effect on Escherichia coli, Staphylococcus aureus and Candida albicans. The measured MIC and MBC of functionalized chestnut-shell-extract AgNPs against E. coli, S. aureus and C. albicans is relatively low, which indicated that the present functionalized chestnut-shell-extract AgNPs are an efficient antimicrobial agent.

Nutritional composition and antioxidant properties of the fruit of Berberis heteropoda Schrenk.

OBJECTIVE: This study assessed the major nutrients and antioxidant properties of Berberis heteropoda Schrenk fruits collected from the Nanshan Mountain area of Urumqi City, Xinjiang Uygur Autonomous Region, China. METHODS AND MATERIALS: We assessed the basic nutrients, including amino acids, minerals, and fatty acids, and determined the total phenol, flavonoid, and anthocyanin contents of the extracts. RESULTS: The analytical results revealed the average water (75.22 g/100 g), total fat (0.506 g/100 g), total protein (2.55 g/100 g), ash (1.31 g/100 g), and carbohydrate (17.72 g/100 g) contents in fresh B. heteropoda fruit, with total phenol, flavonoid, and anthocyanin contents of B. heteropoda fruits at 68.55 mg gallic acid equivalents/g, 108.42 mg quercetin equivalents/g, and 19.83 mg cyanidin-3-glucoside equivalent/g, respectively. Additionally, UPLC-Q-TOF-MSE analysis of polyphenols in B. heteropoda fruit revealed 32 compounds. CONCLUSION: B. heteropoda fruits may have potential nutraceutical value and represent a potential source of nutrition and antioxidant phytochemicals in the human diet.

Sparse representation based SAR vehicle recognition along with aspect angle.

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.