The reaction of NH-indazoles with 1-fluoro-2,4-dinitrobenzene: the unusual formation of benzotriazole-N-oxides

15 páginas, 19 figuras, 7 tablas.When N-unsubstituted indazoles, like indazole itself, reacted with 1-fluoro-2,4-dinitrobenzene or 1-chloro-2,4,6-trinitrobenzene, three products were obtained whose structures were determined by X-ray diffraction. Besides the two N-substituted nitroaryl derivatives, a third compound was obtained with the same molecular formula (C13H8N4O4) to which was assigned the structure of a derivative of benzotriazole N-oxide. With the combined use of crystallography, NMR and DFT calculations this reaction was studied with special stress on the mechanism of formation of the benzotriazole-N-oxide.This work was supported by the Spanish Ministerio de Economía y Competitividad (MAT2010-15094, Factoría de Cristalización–Consolider Ingenio 2010) and FEDER funding. We also thank the Ministerio de Ciencia e Innovación (Project No. CTQ 2009-13129-C02-02) and the Comunidad Autónoma de Madrid (Project MADRISOLAR2, ref. S2009/PPQ-1533) for continuing support. F. P. C. thanks the Spanish MICINN (Grants CTQ2010-16959 and Ingenio-Consolider CSD2007-00006) and the Basque Government (GV-EJ, Grant IT-324-07) for financial support.Peer reviewe

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts