Machine-learning guided venom induced dermonecrosis analysis tooL: VIDAL

 

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Autores: Laprade, William Michael, Bartlett, Keirah E., Christensen, Charlotte Risager, Kazandjian, Taline D., Patel, Rohit N., Crittenden, Edouard, Dawson, Charlotte A., Mansourvar, Marjan, Wolff, Darian Stephan, Fryer, Thomas, Laustsen Kiel, Andreas Hougaard, Casewell, Nicholas R., Gutiérrez, José María, Hall, Steven Robert, Jenkins, Timothy Patrick
Formáid: artículo original
Fecha de Publicación:2023
Cur Síos:Snakebite envenoming is a global public health issue that causes significant morbidity and mortality, particularly in low-income regions of the world. The clinical manifestations of envenomings vary depending on the snake’s venom, with paralysis, haemorrhage, and necrosis being the most common and medically relevant effects. To assess the efficacy of antivenoms against dermonecrosis, a preclinical testing approach involves in vivo mouse models that mimic local tissue effects of cytotoxic snakebites in humans. However, current methods for assessing necrosis severity are time-consuming and susceptible to human error. To address this, we present the Venom Induced Dermonecrosis Analysis tool (VIDAL), a machine-learning-guided image-based solution that can automatically identify dermonecrotic lesions in mice, adjust for lighting biases, scale the image, extract lesion area and discolouration, and calculate the severity of dermonecrosis. We also introduce a new unit, the dermonecrotic unit (DnU), to better capture the complexity of dermonecrosis severity. Our tool is comparable to the performance of state-of-the-art histopathological analysis, making it an accessible, accurate, and reproducible method for assessing dermonecrosis. Given the urgent need to address the neglected tropical disease that is snakebite, high-throughput technologies such as VIDAL are crucial in developing and validating new and existing therapeutics for this debilitating disease.
País:Kérwá
Institiúid:Universidad de Costa Rica
Repositorio:Kérwá
Teanga:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/104204
Rochtain Ar Líne:https://hdl.handle.net/10669/104204
http://dx.doi.org/10.1038/s41598-023-49011-6
Palabra clave:dermonecrosis
snakebite envenoming
machine learning
VIDAL
venom
necrosis
mouse models
toxinology
antivenom
neglected tropical disease