Vladimir Pavlov | PubMed Health

PubMed2026

Antivenomic and Proteomic Assessment of Inter- and Intrapopulation Venom Variations in Nikolsky's Adder Vipera nikolskii: Comparison to Common Adder Vipera berus.

Snake venom variation has important clinical implications, yet individual-level venomics remains limited. We investigated inter- and intrapopulation variability in forest-steppe adder Vipera nikolskii and its recognition by commercial V. berus antivenom using proteomic and immunological approaches. Venoms from 12 individual V. nikolskii specimens representing two geographically distinct populations (BG and KM), together with three pooled V. nikolskii and one pooled V. berus samples were analyzed by LC-MS/MS, ELISA, Western blot, and pull-down assays. Multivariate analysis revealed relative homogeneity in BG and pronounced heterogeneity in KM venoms. Area-based proteomics revealed V. berus venom enrichment in PLA2 (34.6%), SVMP (14.6%), and CRiSP (15.6%), whereas V. nikolskii venoms were more variable. Pooled V. nikolskii venoms showed SVMP abundance (35.2-41.1%), contrasting with lower levels in individual samples. Antivenom binding was stronger for V. berus but weaker and more variable across individual and pooled V. nikolskii samples. Antivenom targeted PLA2/VEGF, CRiSP (only in V. berus), and Kunitz-type proteins. In vivo neutralization assay demonstrated strong protection against V. berus but not V. nikolskii venom. These findings reveal substantial compositional and antigenic variability in V. nikolskii venoms, highlight discrepancies between pooled and individual ones, and underscore the need for region-specific and functionally validated antivenom evaluation.

Animals↗

PubMedDate not supplied

Environmental mapping based on spatial variability.

Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Multiple schemes of image classification used in environmental mapping are either traditionally statistical or heuristic. While the former methods do not take account of spatial variability in space and aerial data, the latter ones does not lend themselves to optimal solutions we present. Novel probabilistic models of piecewise-homogeneous images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Parameters of the model are estimated by using a stochastic approximation technique. Its convergence to the desired values is studied experimentally. Addition of spatial attributes appears to be necessary in most areas where the differences in spatial data between regions in the image occur. Experiments in generating the pairs of images and environmental maps and in segmenting the simulated as well as real images are discussed.

Environment↗