The trans isomers interacted well CA3 supplier with HIV-1 RI through H-bonding with amino
acids, like Lys101, Lys103, His235, Tyr318, constituting the non-nucleoside inhibitor binding pocket (NNIBP) during docking experiments. However, the compounds showed very little activity when subjected to in vitro anti-HIV-1 screening using beta-galactosidase assay (TZM-bl cells) and GFP quantification (CEM-GFP cells). The very low level of in vitro HIV inhibition, in comparison to predicted EC(50) values on the basis of computational studies, during CEM-GFP screening using AZT as positive control indicated that probably the HIV RI is not the viral target and the molecules work through some different mechanism. (C) 2011 Elsevier Masson SAS. All rights reserved.”
“Leaf curl disease of tobacco (TbLCD) is endemic in India. A monopartite Begomovirus, a betasatellite and an alphasatellite were found associated with the disease in Pusa, Bihar. The DNA-A of the Begomovirus associated with TbLCD in Pusa, Bihar was found to comprise of 2707 nt with a typical Old World begomovirus-like
genome organization. The full-length sequence of DNA-A [HQ180391] showed that the Pusa isolate is a newly described member of the genus Begomovirus, as it had < 89% sequence homology with DNA-A of all the known begomoviruses. Linsitinib molecular weight The isolate is tentatively named as Tobacco leaf curl Pusa virus [India:Pusa:2010]. The betasatellite (HQ180395) associated with TbLCD in Pusa was identified as a variant of Tomato leaf curl Bangladesh betasatellite [IN:Raj:03], with which it shared 90.4% sequence LDK378 Protein Tyrosine Kinase inhibitor identity. The alphasatellite (HQ180392) associated with the disease had highest
87% nucleotide sequence identity with Tomato leaf curl alphasatellite. The Begomovirus, betasatellite, and alphasatellite associated with TbLCD in Pusa, Bihar, India were found to be recombinants of extant begomoviruses, betasatellites and alphasatellites spreading in the Indian sub-continent and South-East Asia.”
“Objective\n\nThis study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hopitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system.\n\nMethods\n\nAP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary.\n\nResults\n\nThis new management platform is now active in AP-HP.