Expression of Lex antigen in Schistosoma japonicum and S.haematobium and immune responses to Lex in infected animals: lack of Lex expression in other trematodes and nematodes

Adults of the human parasitic trematode Schistosoma mansoni, which causes hepatosplenic/intestinal complications in humans, synthesize glycoconjugates containing the Lewis x (Lex) Galbeta1-->4(Fucalpha1-- >3)GlcNAcbeta1-->R, but not sialyl Lewis x (sLex), antigen. We now report on our analyses of Lexand sLexexpression in S.haematobium and S.japonicum, which are two other major species of human schistosomes that cause disease, and the possible autoimmunity to these antigens in infected individuals. Antigen expression was evaluated by both ELISA and Western blot analyses of detergent extracts of parasites using monoclonal antibodies. Several high molecular weight glycoproteins in both S. haematobium and S. japonicum contain the Lexantigen, but no sialyl Lexantigen was detected. In addition, sera from humans and rodents infected with S.haematobium and S.japonicum contain antibodies reactive with Lex. These results led us to investigate whether Lexantigens are expressed in other helminths, including the parasitic trematode Fasciola hepatica , the parasitic nematode Dirofilaria immitis (dog heartworm), the ruminant nematode Haemonchus contortus , and the free-living nematode Caenorhabditis elegans . Neither Lexnor sialyl-Lexis detectable in these other helminths. Furthermore, none of the helminths, including schistosomes, express Lea, Leb, Ley, or the H- type 1 antigen. However, several glycoproteins from all helminths analyzed are bound by Lotus tetragonolobus agglutinin , which binds Fucalpha1-->3GlcNAc, and Wisteria floribunda agglutinin, which binds GalNAcbeta1-->4GlcNAc (lacdiNAc or LDN). Thus, schistosomes may be unique among helminths in expressing the Lexantigen, whereas many different helminths may express alpha1,3-fucosylated glycans and the LDN motif.      

Nifedipine loaded chitosan microspheres prepared by emulsification phase-separation

A high yield of nifedipine-chitosan microspheres could be obtained using an emulsification phase-separation method. A high level of entrapment of nifedipine in the microspheres was achieved. The microspheres exhibited excellent swelling properties. Differential scanning calorimetry, X-ray diffractometry, and scanning electron microscopy confirmed that at 1.84% loading, nifedipine was dispersed molecularly. The microspheres exhibited faster release at low loadings compared to high loadings. Fitting the data to the coupled Fickian/case II equation, showed that at low loadings polymer relaxation coefficients (k₂) were high. As the polymer content increased in the microspheres, the value of n (diffusional exponent characteristic of the release mechanism) approached one, which is indicative of zero order.     

Predicting breast cancer using an expression values weighted clinical classifier

Background

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier.LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.

Results

We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.

Conclusions

Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.     

Analysis of copia sequence variation within and between Drosophila species

The sequences of the 5' long-terminal repeat (LTR) and adjacent leader regions of 27 full-length copia elements isolated from natural populations of Drosophila melanogaster, D. simulans, and D. mauritiana are presented. Phylogenetic analyses indicate that although D. melanogaster copia elements are distinct from those of D. simulans and D. mauritiana, the elements of these latter two species are not distinguishable from one another. LTRs and adjacent 5' leader regions of elements isolated from D. simulans and D. mauritiana are structurally similar to one another and carry substantial deletional variation mapping to regions previously identified as being of potential importance for copia expression.      

Target highlights in CASP13: Experimental target structures through the eyes of their authors

The functional and biological significance of selected CASP13 targets are described by the authors of the structures. The structural biologists discuss the most interesting structural features of the target proteins and assess whether these features were correctly reproduced in the predictions submitted to the CASP13 experiment.