0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Alpha-Pinene Exerts Antiseizure Effects by Preventing Oxidative Stress and Apoptosis in the Hippocampus in a Rat Model of Temporal Lobe Epilepsy Induced by Kainate

      ,
      Molecular Neurobiology
      Springer Science and Business Media LLC

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" id="d8597484e69">Oxidative stress and apoptosis following seizures play pivotal roles in the consequences of repeated seizures. Beneficial effects of alpha-pinene (APN) have been reported in some experimental models of neurodegenerative diseases. However, its neuroprotective efficacy in a rat model of temporal lobe epilepsy (TLE) induced by kainic acid (KA) has remained unexplored. We aimed to explore the possible antiseizure effects of APN pretreatment and underlying molecular mechanisms in a rat model of TLE induced by KA. TLE was induced in male Wistar rats by intracerebroventricular injection of KA. APN at a dose of 50 mg/kg/day was intraperitoneally injected for 2 weeks before induction of TLE. One day after the induction of TLE, behavioral expressions of seizure were recorded and scored using Racine's scale. Furthermore, the hippocampal levels of oxidative stress markers, B-cell lymphoma 2 (Bcl2), BCL2-associated X protein (BAX), and c-Jun N-terminal kinase (JNK) protein levels were also assessed. Histopathological assessment in the hippocampus was performed with Nissl staining 5 days following induction of TLE. The results revealed that APN pretreatment alleviated epileptic seizures, diminished oxidative stress indicators, blocked the mitochondrial apoptotic pathway via decreasing BAX and raising BCL2 protein levels in the hippocampus at least partly through inhibiting JNK activity, and decreased neuronal death in the CA3 and hilus regions. These findings reveal that APN pretreatment mitigates KA-induced seizures by blocking oxidative stress and neuronal damage factors. It can be concluded that APN has a potent potential to be considered an antiseizure medication, but it needs further investigation. </p>

          Related collections

          Most cited references53

          • Record: found
          • Abstract: not found
          • Article: not found

          Modification of seizure activity by electrical stimulation: II. Motor seizure

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Random allocation software for parallel group randomized trials

            Background Typically, randomization software should allow users to exert control over the different aspects of randomization including block design, provision of unique identifiers and control over the format and type of program output. While some of these characteristics have been addressed by available software, none of them have all of these capabilities integrated into one package. The main objective of the Random Allocation Software project was to enhance the user's control over different aspects of randomization in parallel group trials, including output type and format, structure and ordering of generated unique identifiers and enabling users to specify group names for more than two groups. Results The program has different settings for: simple and blocked randomizations; length, format and ordering of generated unique identifiers; type and format of program output; and saving sessions for future use. A formatted random list generated by this program can be used directly (without further formatting) by the coordinator of the research team to prepare and encode different drugs or instruments necessary for the parallel group trial. Conclusions Random Allocation Software enables users to control different attributes of the random allocation sequence and produce qualified lists for parallel group trials.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The kainic acid model of temporal lobe epilepsy.

              The kainic acid model of temporal lobe epilepsy has greatly contributed to the understanding of the molecular, cellular and pharmacological mechanisms underlying epileptogenesis and ictogenesis. This model presents with neuropathological and electroencephalographic features that are seen in patients with temporal lobe epilepsy. It is also characterized by a latent period that follows the initial precipitating injury (i.e., status epilepticus) until the appearance of recurrent seizures, as observed in the human condition. Finally, the kainic acid model can be reproduced in a variety of species using either systemic, intrahippocampal or intra-amygdaloid administrations. In this review, we describe the various methodological procedures and evaluate their differences with respect to the behavioral, electroencephalographic and neuropathological correlates. In addition, we compare the kainic acid model with other animal models of temporal lobe epilepsy such as the pilocarpine and the kindling model. We conclude that the kainic acid model is a reliable tool for understanding temporal lobe epilepsy, provided that the differences existing between methodological procedures are taken into account. Copyright © 2013 Elsevier Ltd. All rights reserved.
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Molecular Neurobiology
                Mol Neurobiol
                Springer Science and Business Media LLC
                0893-7648
                1559-1182
                June 2023
                February 25 2023
                June 2023
                : 60
                : 6
                : 3227-3238
                Article
                10.1007/s12035-023-03274-2
                36840843
                ba873250-5797-4bbd-8ded-7ac534dd98d3
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

                History

                Comments

                Comment on this article