Preparation of magnetic composites and their dimethyl arsonic acid adsorption performances.

Dimethyl arsonic acid, the most common organic arsenic pollutant, is widely present in the environment and seriously threatens the safety of drinking water. Syntheses of magnetite, magnetic bentonite, and magnetic ferrihydrite via hydrothermal methods, and the magnetic composites were examined using XRD, BET, VSM, and SEM. SEM images revealed that many monodispersible pellets were attached to the surface of magnetic bentonite. The magnetic ferrihydrite contained abundant pores and had a rich pore structure, which expanded the specific surface area of the original magnetite. The specific surface areas of the magnetic bentonite and magnetic ferrihydrite were 65.17 and 220.30 m2·g-1, respectively. The adsorption kinetics and adsorption isotherms of dimethyl arsonic acid on magnetic composites were studied. The adsorption of dimethyl arsonic acid on the magnetic composites conformed to the pseudo-second-order model and Freundlich isothermal adsorption model. By comparing the isotherms of the adsorption of dimethyl arsonic acid by the magnetic composites at pH values of 3, 7, and 11, respectively, it was found that the adsorption of dimethyl arsonic acid was the greatest at neutral pH of 7. The adsorption mechanism was analyzed via zeta potential determination, Fourier transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS). The zeta potential results revealed that magnetic bentonite electrostatic activity occurred with dimethyl arsonic acid, and the magnetic ferrihydrite indicated a coordination complex with dimethyl arsonic acid. The XPS results revealed that the Fe-O bonds on the surfaces of the magnetic ferrihydrite had coordination complexation effects on the As-O of the dimethyl arsonic acid.

Diagnostic Value of Serum DNASE1L3 in Hepatitis B Virus-Related Hepatocellular Carcinoma.

BACKGROUND: Deoxyribonuclease 1-like 3 (DNASE1L3) is an endonuclease associated with many autoimmune diseases and tumors. However, the serum DNASE1L3 level in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) remains unreported. Thus, this study compared the diagnostic value of DNASE1L3 and alpha-feto-protein (AFP) individually and in combination in HBV-related HCC. METHODS: The study population consisted of 88 patients with HBV-related HCC, 80 patients with HBV-related liver cirrhosis (LC) and 88 control subjects. The serum DNASE1L3 levels were measured using an enzyme-linked immunosorbent assay. The serum AFP was also assayed. RESULTS: Our data showed that the serum DNASE1L3 levels were significantly higher in patients with HBV-related HCC than in the healthy controls and patients with LC. When the two biomarkers were analyzed individually, the receiver operating characteristic curve analysis showed that the areas under the curve of DNASE1L3 and AFP were 0.898 and 0.866, respectively. When DNASE1L3 and AFP were combined, the area under the curve was 0.951. The sensitivities of DNASE1L3 and AFP were 72.73% and 74.81%, respectively, and the specificities were 93.18% and 92.05%, respectively, in the diagnosis of HBV-related HCC. The sensitivity of the two combined could be improved to 89.77%. However, no correlation was found between serum DNASE1L3 and AFP in HBV-related HCC patients (r = 0.005, p = 0.734). CONCLUSIONS: Serum DNASE1L3 has high sensitivity and specificity in the diagnosis of HCC. DNASE1L3 combined with AFP has higher sensitivity and can improve the diagnostic efficiency of HBV-related HCC.

Nogo­66 promotes ß­amyloid protein secretion via NgR/ROCK­dependent BACE1 activation.

The generation of ß­amyloid protein (Aß) is considered a key step in the pathogenesis of Alzheimer's disease (AD) and the regulation of its production is an important therapeutic strategy. It was hypothesized in the present study that Nogo­A may be involved in AD and may regulate the generation of Aß. Nogo­A is known to act as a major inhibitor of neuron regeneration in the adult central nervous system. A recent study indicated that Nogo­A is associated with AD; however, the underlying effect and molecular mechanisms remain largely elusive. In the present study, the potential effects of Nogo­A on AD were investigated. ELISA was used to detect the levels of Aß, enzymatic activity detection kits were used to determine the activity of secretase enzymes in amyloid precursor protein (APP) metabolism, and western blot analysis was used to detect the expression levels of proteins associated with the APP processing and Nogo­A/Nogo­66 receptor (NgR) signaling pathways. The results revealed that Nogo­66, the major inhibitory region of Nogo­A, promoted neuronal Aß secretion by increasing the activity of ß­secretase 1 via the NgR/Rho­associated coiled­coil containing kinases pathway in a dose­dependent manner. The present data suggested that Nogo­A may facilitate the onset and development of AD by promoting Aß secretion, providing information on a potential novel target for AD therapy.

A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method.

Breast cancer resistance protein (BCRP) is one of the key multi-drug resistance proteins, which significantly influences the therapeutic effects of many drugs, particularly anti-cancer drugs. Thus, distinguishing between substrates and non-substrates of BCRP is important not only for clinical use but also for drug discovery and development. In this study, a prediction model of the substrates and non-substrates of BCRP was developed using a modified support vector machine (SVM) method, namely GA-CG-SVM. The overall prediction accuracy of the established GA-CG-SVM model is 91.3% for the training set and 85.0% for an independent validation set. For comparison, two other machine learning methods, namely, C4.5 DT and k-NN, were also adopted to build prediction models. The results show that the GA-CG-SVM model is significantly superior to C4.5 DT and k-NN models in terms of the prediction accuracy. To sum up, the prediction model of BCRP substrates and non-substrates generated by the GA-CG-SVM method is sufficiently good and could be used as a screening tool for identifying the substrates and non-substrates of BCRP.

Combined SVM-based and docking-based virtual screening for retrieving novel inhibitors of c-Met.

Aberrant c-Met activation has been demonstrated to be implicated in tumorigenesis and anti-cancer drug resistance. Discovery of c-Met inhibitors has attracted much attention in recent years. In this study, a support vector machine (SVM) classification model that discriminates c-Met inhibitors and non-inhibitors was first developed. Evaluation through screening a test set indicates that combined SVM-based and docking-based virtual screening (SB/DB-VS) considerably increases hit rate and enrichment factor compared with the individual methods. Thus the combined SB/DB-VS approach was adopted to screen PubChem, Specs, and Enamine for c-Met inhibitors. 75 compounds were selected for in vitro assays. Eight compounds display a good inhibitory potency against c-Met. Five of them are found to have novel scaffolds, implying a good potential for further chemical modification.

Pharmacophore modeling studies of type I and type II kinase inhibitors of Tie2.

In this study, chemical feature based pharmacophore models of type I and type II kinase inhibitors of Tie2 have been developed with the aid of HipHop and HypoRefine modules within Catalyst program package. The best HipHop pharmacophore model Hypo1_I for type I kinase inhibitors contains one hydrogen-bond acceptor, one hydrogen-bond donor, one general hydrophobic, one hydrophobic aromatic, and one ring aromatic feature. And the best HypoRefine model Hypo1_II for type II kinase inhibitors, which was characterized by the best correlation coefficient (0.976032) and the lowest RMSD (0.74204), consists of two hydrogen-bond donors, one hydrophobic aromatic, and two general hydrophobic features, as well as two excluded volumes. These pharmacophore models have been validated by using either or both test set and cross validation methods, which shows that both the Hypo1_I and Hypo1_II have a good predictive ability. The space arrangements of the pharmacophore features in Hypo1_II are consistent with the locations of the three portions making up a typical type II kinase inhibitor, namely, the portion occupying the ATP binding region (ATP-binding-region portion, AP), that occupying the hydrophobic region (hydrophobic-region portion, HP), and that linking AP and HP (bridge portion, BP). Our study also reveals that the ATP-binding-region portion of the type II kinase inhibitors plays an important role to the bioactivity of the type II kinase inhibitors. Structural modifications on this portion should be helpful to further improve the inhibitory potency of type II kinase inhibitors.