Snake Venom and Antivenom Pharmacology

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  SNAKE VENOM & ANTIVENOM Chemistry, Composition, Mechanisms and Pharmacology Abstract This integrated teaching session for Phase III MBBS students focused on the pharmacology and toxicology of snake venom and the principles of antivenom therapy. The lecture covered the chemistry, composition, mechanisms of action, and pharmacological effects of snake venoms and antivenoms. Rational use of antivenoms, indications, administration protocols, adverse reactions, and supportive management strategies were discussed. The session also highlighted the translational importance of venom-derived compounds in modern drug development. Recent advances in antivenom research, including recombinant human monoclonal antibodies, toxin-specific inhibitors, synthetic antibody technologies, and next-generation broad-spectrum antivenoms, were reviewed. Current discoveries and emerging approaches aimed at improving efficacy, safety, affordability, and accessibility of antivenom therapy were also explor...

Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna

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Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna

Abstract

The biota of cave habitats faces heightened conservation risks, due to geographic isolation and high levels of endemism. Molecular datasets, in tandem with ecological surveys, have the potential to precisely delimit the nature of cave endemism and identify conservation priorities for microendemic species. Here, we sequenced ultraconserved elements of Tegenaria within, and at the entrances of, 25 cave sites to test phylogenetic relationships, combined with an unsupervised machine learning approach for detecting species. Our analyses identified clear and well-supported genetic breaks in the dataset that accorded closely with morphologically diagnosable units. Through these analyses, we also detected some previously unidentified, potential cryptic morphospecies. We then performed conservation assessments for seven troglobitic Israeli species of this genus and determined five of these to be critically endangered.

Steiner, H.G., Aharon, S., Ballesteros, J. et al. Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna. Conserv Genet (2024). https://doi.org/10.1007/s10592-024-01627-5