Necrodes littoralis (Coleoptera: Silphidae) visiting and breeding on a carcass in Italy.

The community that progressively colonizes a decaying corpse can be considered a small ecosystem mostly composed of sarcosaprophagous arthropods belonging to the orders Diptera and Coleoptera. Studies on these species are often performed through animal models to obtain data on their succession, behaviour and life cycle, together with information on habitat, corpse conditions, season and association with other species. These data may be relevant for forensic investigations, especially concerning the estimation of Post Mortem Interval (PMI). An investigation on the sarcosaprophagous insect community in a rural area was set in Calabria (Southern Italy), using a pig, Sus scrofa Linnaeus, 1758 (Artiodactyla: Suidae) as experimental model. Analyses of the community of Diptera and Coleoptera revealed the massive presence of Necrodes littoralis (Linnaeus, 1758) (Coleoptera: Silphidae). Adults of this species reached the carcass during the bloated stage and a large amount of larvae was detected from the decay stage onwards, simultaneous to the sharp decrease in dipteran larvae and pupae. The occurrence and the activity of N. littoralis should be considered to avoid misinterpretation and errors in estimating PMI in forensic investigation.

Necrodes littoralis (Coleoptera: Silphidae) visiting and breeding on a carcass in Italy

@#The community that progressively colonizes a decaying corpse can be considered a small ecosystem mostly composed of sarcosaprophagous arthropods belonging to the orders Diptera and Coleoptera. Studies on these species are often performed through animal models to obtain data on their succession, behaviour and life cycle, together with information on habitat, corpse conditions, season and association with other species. These data may be relevant for forensic investigations, especially concerning the estimation of Post Mortem Interval (PMI). An investigation on the sarcosaprophagous insect community in a rural area was set in Calabria (Southern Italy), using a pig, Sus scrofa Linnaeus, 1758 (Artiodactyla: Suidae) as experimental model. Analyses of the community of Diptera and Coleoptera revealed the massive presence of Necrodes littoralis (Linnaeus, 1758) (Coleoptera: Silphidae). Adults of this species reached the carcass during the bloated stage and a large amount of larvae was detected from the decay stage onwards, simultaneous to the sharp decrease in dipteran larvae and pupae. The occurrence and the activity of N. littoralis should be considered to avoid misinterpretation and errors in estimating PMI in forensic investigation.

Chemometric rationalization of the structural and physicochemical basis for selective cyclooxygenase-2 inhibition: toward more specific ligands.

The discovery that proinflammatory prostaglandins are produced by cyclooxygenase-2 (COX-2), an inducible isoform of the constitutive cyclooxygenase-1 (COX-1), opened a new frontier in the treatment of inflammatory diseases, because the selective inhibition of COX-2 can lead to therapeutically effective compounds which do not have the common side effects of classical non-steroidal antiinflammatory drugs (NSAIDs). Different crystallographic structures of both free COX-1 and COX-2 as well as complexes with inhibitors have been solved. Because of the great similarity between the two enzymes, it is difficult to detect the most important structural and physicochemical features that would be useful for designing inhibitors with an improved selectivity. In this paper we describe the application of a chemometric procedure to the study of COX-2 selective inhibition. This method, developed to reveal the most suitable regions of isoenzymes for the design of selective ligands, also has a very practical utility. GRID multivariate characterization of the enzymes and subsequent Principal Component Analysis (PCA) of the descriptor variables allow the identification of chemical groups that could be added to a core template structure to increase ligand selectivity.

Chemometric methodologies in a quantitative structure-activity relationship study: the antibacterial activity of 6-aminoquinolones.

The paper illustrates the chemometric strategies appropriate for extracting information from a large amount of biological data regarding the antibiotic activity of 6-aminoquinolones. The unique framework based on principal component analysis, projection onto latent structures, and response surface methodologies permits the structure-activity correlations to be shown and to suggest new compounds for further testing. The low activity of the suggested molecules points out the limitations of quantitative structure-activity relationship models when the training set is not properly designed in order to balance all the structural variations taken into account.

A comparative chemometric study of QSAR descriptors.

Traditional descriptors for Qsar models are compared with two alternative blocks obtained by computer chemistry tools: electronic densities and principal properties. All three sets show similar prediction abilities by PLS modelling.

Chemometric approach in a QSAR study: the antibacterial and antimicotic activities of benzofused heteroaromatic derivatives.

In order to establish quantitative relationships between the antibacterial activities of a number of thienyl- and furyl-benzimidazoles and benzoxazoles, the biological data were measured homogeneously for a selected set of 16 representative compounds of the available set of 103. The data were analyzed by the PLS method. The results of linear PLS modelling and PLS response surface modelling permitted a straightforward interpretation of the structural features relevant to the activities and the prediction of a possible optimal structure.

Response surfaces for modelling biological activities by principal properties of substituents: the CARSO procedure.

Three literature data sets are modelled by PLS response surfaces in terms of the principal properties of organic substituents by means of the CARSO procedure. The models permit one to rationalize the biological data, to predict new activities, and to detect optimal structures.