The endocannabinoid system (ECS) is related to several physiological processes, associated to the modulation of brain excitability, with impact in the expression of susceptibility and control of epileptic seizures. The cannabinoid receptor type 1 (CB1R) is widely expressed in the brain, especially in forebrain limbic structures. Changes in CB1R expression are associated with epileptic seizures in animal models and humans. The Wistar Audiogenic Rat (WAR) strain is a genetic model of epilepsy capable of mimicking tonic-clonic and limbic seizures in response to intense sound stimulation. The WAR strain presents several behavioral and physiological alterations associated with seizure susceptibility, but the ECS has never been explored in this strain. Therefore, the aim of the present study was to characterize CB1R expression in forebrain limbic structures important to limbic seizure expression in WARs. We used a detailed anatomical analysis to assess the effects of acute and chronic audiogenic seizures on CB1R expression in several layers and regions of hippocampus and amygdala. WARs showed increased CB1R immunostaining in the inner molecular layer of the hippocampus, when compared to control Wistar rats. Acute and chronic audiogenic seizures increased CB1R immunostaining in several regions of the dorsal hippocampus and amygdala of WARs. Also, changes in CB1R expression in the amygdala, but not in the hippocampus, were associated with limbic recruitment and limbic seizure severity in WARs. Our results suggest that endogenous alterations in CB1R immunostaining in WARs could be associated with genetic susceptibility to audiogenic seizures. We also demonstrated CB1R neuroplastic changes associated with acute and chronic seizures in the amygdala and hippocampus. Moreover, the present study brings important information regarding CB1R and seizure susceptibility in a genetic model of seizures and supports the relationship between ECS and epilepsy.
Almost 115 years ago, Alois Alzheimer described Alzheimer’s disease (AD) for the first time. Since then, many hypotheses have been proposed. However, AD remains a severe health public problem. The current medical approaches for AD are limited to symptomatic interventions and the complexity of this disease has led to a failure rate of approximately 99.6%in AD clinical trials. In fact, no new drug has been approved for AD treatment since 2003. These failures indicate that we are failing in mimicking this disease in experimental models. Although most studies have focused on the amyloid cascade hypothesis of AD, the literature has made clear that AD is rather a multifactorial disorder. Therefore, the persistence in a single theory has resulted in lost opportunities. In this review, we aim to present the striking points of the long scientific path followed since the description of the first AD case and the main AD hypotheses discussed over the last decades. We also propose insulin resistance as a common link between many other hypotheses.
Drug-resistant epilepsy remains to this day as a highly prevalent condition affecting around one-third of patients with epilepsy, despite all the research and the development of several new antiseizure medications (ASMs) over the last decades. Epilepsies are multifactorial complex diseases, commonly associated with psychiatric, neurological, and somatic comorbidities. Thus, to solve the puzzling problem of pharmacoresistance, the diagnosis and modeling of epilepsy and comorbidities need to change toward a complex system approach. In this review, we have summarized the sequence of events for the definition of epilepsies and comorbidities, the search for mechanisms, and the major hypotheses of pharmacoresistance, drawing attention to some of the many converging aspects between the proposed mechanisms, their supporting evidence, and comorbidities-related alterations. The use of systems biology applied to epileptology may lead to the discovery of new targets and the development of new ASMs, as may advance our understanding of the epilepsies and their comorbidities, providing much deeper insight on multidrug pharmacoresistance.
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