Although animal models have helped to elaborate meaningful hypotheses about the pathophysiology of sudden and unexpected death in epilepsy (SUDEP), specific prevention strategies are still lacking, potentially reflecting the limitations of these models and the intrinsic difficulties of investigating SUDEP. The interpretation of preclinical data and their translation to diagnostic and therapeutic developments in patients thus require a high level of confidence in their relevance to model the human situation. Preclinical models of SUDEP are heterogeneous and include rodent and nonrodent species. A critical aspect is whether the animals have isolated seizures exclusively induced by a specific trigger, such as models where seizures are elicited by electrical stimulation, pharmacological intervention, or DBA mouse strains, or whether they suffer from epilepsy with spontaneous seizures, with or without spontaneous SUDEP, either of nongenetic epilepsy etiology or from genetically based developmental and epileptic encephalopathies. All these models have advantages and potential disadvantages, but it is important to be aware of these limitations to interpret data appropriately in a translational perspective. The majority of models with spontaneous seizures are of a genetic basis, whereas SUDEP cases with a genetic basis represent only a small proportion of the total number. In almost all models, cardiorespiratory arrest occurs during the course of the seizure, contrary to that in patients observed at the time of death, potentially raising the issue of whether we are studying models of SUDEP or models of periseizure death. However, some of these limitations are impossible to avoid and can in part be dependent on specific features of SUDEP, which may be difficult to model. Several preclinical tools are available to address certain gaps in SUDEP pathophysiology, which can be used to further validate current preclinical models.
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http://dx.doi.org/10.1111/epi.17812 | DOI Listing |
World J Surg Oncol
January 2025
Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China.
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View Article and Find Full Text PDFMicrobiome
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Background: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.
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Mosquito-borne flaviviruses represent a public health challenge due to the high-rate endemic infections, severe clinical outcomes, and the potential risk of emerging global outbreaks. Flavivirus disease pathogenesis converges on cellular factors from vectors and hosts, and their interactions are still unclear. Exosomes and microparticles are extracellular vesicles released from cells that mediate the intercellular communication necessary for maintaining homeostasis; however, they have been shown to be involved in disease establishment and progression.
View Article and Find Full Text PDFWorld J Surg Oncol
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