Nociception is an essential response for organisms to avoid potential harm and promote survival. Its molecular determinants are largely conserved across Eumetazoa. TRPV receptors are polymodal ion channels exhibiting selective peripheral expression and functional coupling that underpins nociception and pain modulation in complex organisms. However, the execution of protective behaviours triggered by TRPVs is also found in species with a simpler nervous organisation, thus encouraging their investigation in invertebrate model organisms to increase understanding of animal nociception. Cephalopods represent an interesting invertebrate phylum with respect to the evolution of the nervous system, whose complexity suggests it might support pain-like states that exist in vertebrates. This possibility is reflected by the inclusion of cephalopods in the UK and EU animal welfare legislations. Despite this, there is poor characterisation of cephalopod molecular nociceptors. For this reason, we used in silico analysis to identify two TRPV channels in Octopus vulgaris genome (Ovtrpv1 and Ovtrpv2). We validated the putative transcript sequences and highlighted prevalent expression in sensory tissues. We investigated the functional competence of these TRPVs by heterologously expressing Ovtrpv1 and Ovtrpv2 cDNA into Caenorhabditis elegans null mutants of the orthologous genes, ocr-2 and osm-9 respectively. Ovtrpvs successfully rescued the aversive response to chemical and mechanical noxious stimuli in the C. elegans mutants, suggesting these receptors are polymodal nociceptors. Additionally, complementary investigation using Xenopus laevis oocytes showed Ovtrpv1 and Ovtrpv2 form an active heteromeric channel gated by nicotinamide. This study highlights Ovtrpvs as an important route to better understand nociceptive detection in cephalopods.
Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models
BackgroundSocial media provides timely proxy signals of mental health, but reliable tweet-level classification of depression subtypes remains challenging due to short, noisy text, overlapping symptomatology,




