Epidural spinal cord stimulation (SCS) is indicated for the treatment of intractable pain and is widely used in clinical practice. In previous basic research, the therapeutic effects of SCS have been demonstrated for epileptic seizure. However, the mechanism has not yet been elucidated. In this study, we investigated the therapeutic effect of SCS and the influence of epileptic seizure. First, SCS in the cervical spine was performed. The rats were divided into four groups: control group and treatment groups with SCS conducted at 2, 50, and 300 Hz frequency. Two days later, convulsions were induced by the intraperitoneal administration of kainic acid, followed by video monitoring to assess seizures. We also evaluated glial cells in the hippocampus by fluorescent immunostaining, electroencephalogram measurements, and inflammatory cytokines such as C-C motif chemokine ligand 2 (CCL2) by quantitative real-time polymerase chain reaction. Seizure frequency and the number of glial cells were significantly lower in the 300 Hz group than in the control group. SCS at 300 Hz decreased gene expression level of CCL2, which induces monocyte migration. SCS has anti-seizure effects by inhibiting CCL2-mediated cascades. The suppression of CCL2 and glial cells may be associated with the suppression of epileptic seizure.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease\'s burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.
METHODS: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.
RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.
CONCLUSIONS: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

BACKGROUND: Epilepsy is the most common neurological disorder among humans after headaches. According to the World Health Organization, approximately 50-65 million individuals were diagnosed with epilepsy throughout the world, and around two million new cases of epilepsy are added to this figure every year.
METHODS: Designed as descriptive and cross-sectional research, this study was performed on 132 elementary school teachers. Training on epilepsy and epileptic seizure was given to teachers. The pretest and posttest research data were collected with the face-to-face interview method. In this process, the epilepsy knowledge scale was used as well as a survey form that had questions designed to find out about teachers\' personal characteristics. The Statistical Package for Social Science 25.0 was utilized in the statistical analysis of research data. In the research, the statistical significance was identified if the p-value was below.05 (p < .05).
RESULTS: Of all teachers participating in the study, 59.1% were female, 90.2% were married, and 47.7% witnessed an epilepsy seizure before. The mean of teachers\' pretest epilepsy knowledge scores was 8.43 ± 4.31 points before the training while the mean of their posttest epilepsy knowledge scores was 12.65 ± 2.48 points after the training. The difference between the means of pretest and posttest scores was statistically significant (p = .000). After the training, there was a statistically significant increase in means of scores obtained by teachers from each item of the epilepsy knowledge scale (p < .05).
CONCLUSIONS: As there was a statistically significant improvement in levels of teachers\' knowledge about both epilepsy and epileptic seizure after the training, it is recommended that the training about the approach to epilepsy and epileptic seizure be given to all teachers, and additionally, including these topics in the course curricula of universities is recommended.

OBJECTIVE: Closure surgery of patent foramen ovale (PFO) has been found to effectively control cryptogenic stroke and migraine, but it is uncertain whether PFO closure could also alleviate epileptic seizures. This study aims to observe the therapeutic effect of PFO closure on epileptic seizures.
METHODS: Since July 11th, 2017, in the neurology department of West China Hospital, Sichuan University, Chengdu, we have been regularly monitoring patients with epilepsy who have undergone PFO closure. The patient\'s clinical information, such as frequency, duration, and severity of seizures, before and after surgery was recorded in detail as well as postoperative safety events.
RESULTS: Of the 31 epilepsy patients who confirmed PFO observed (27 cases were drug-resistant epilepsy, 87.10%), average age of surgery was 23.74 years, and 12 cases were female (38.71%). After one-year follow-up, 26 patients (83.87%) achieved remission of seizure frequency, and 22 of whom (70.97%) experienced a remission of more than 50%. Additionally, compared to before surgery, 22 cases (70.97%) reported a decrease in the average seizure duration, and 20 cases (64.52%) reported a reduction in seizure severity. In the seizure indicators of frequency, average duration and severity, significant differences were identified between preoperative and postoperative comparisons with all test p values were <0.05. Furthermore, no serious safety events were reported except for one patient who briefly reported chest pain, and all patients expressed effective PFO closure.
CONCLUSIONS: The PFO closure has been shown for the first time to result in a significant reduction in the frequency, duration, and severity of seizures. Patients with drug-resistant epilepsy and PFO with a large shunt are ideal candidates for undergoing PFO closure.
CONCLUSIONS: Since PFO closure was found to have a good therapeutic effect on cryptogenic stroke and migraine, it has become a credible complementary therapy for the treatment of neurological diseases, and drug-resistant epilepsy with PFO is expected to become the next target disease that PFO closure could significantly improve.

[This corrects the article DOI: 10.3389/fendo.2023.1220957.].

Hypertriglyceridemia-induced acute pancreatitis seldom occurs in the second trimester of pregnancy with gestational diabetes mellitus. For these patients, the existing knowledge on concomitant hyperglycemia is not sufficient. We report a case of abruptio placentae and epileptic seizure following perinatal hyperglycaemia in woman with gestational diabetes mellitus and hypertriglyceridemia-induced acute pancreatitis. The occurrence of abruptio placentae and epileptic seizure may be associated with concomitant hyperglycemia, and the epileptic seizure was terminated after she underwent treatment with insulin. We should pay more attention to the adverse effects of perinatal hyperglycemia and continue to give appropriate insulin treatment even if patients have passed the acute phase of hypertriglyceridemia-induced acute pancreatitis.

Epilepsy is one of the most common chronic neurological syndromes in dogs and has serious implications for the quality of life of both the dogs and owners. Seizure-precipitating factors (SPFs) (also termed \"triggers\" or \"provocative factors\") have been studied and reported in both humans and dogs with idiopathic epilepsy. In dogs stress, hormones, sleep deprivation, and the weather have been reported as SPFs. The Border Collie (BC) is a breed of dog that is predisposed to idiopathic epilepsy, and the outcome is often poor. BC is described as a very sensitive dog with a strong focus on their owners, and this may have an influence on their and their owners\' stress level. In this article, we described six unrelated BCs with idiopathic epilepsy in which several remarkable SPFs were identified, and avoiding them improved the outcome of these dogs. The possible SPFs were different for each dog. The SPFs were, among others, the other dog in the family, the lack of intellectual challenge, the presence of an autistic child, a busy street, the relation with the owner, and throwing a ball at the beach. These cases illustrate that recognizing the SPF(s) and taking measures with regard to management can lead to a reduction in epileptic seizure frequency or even achieving seizure freedom.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

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Background Patients with drug-resistant epilepsy commonly undergo stereo-electroencephalography (SEEG) intracranial monitoring for surgical evaluation. Our current practice of defining the epileptogenic zone relies heavily on recognizing the seizure onset zone (SOZ), but the clinical significance of interictal epileptiform discharges (IEDs) is not well established. Methodology We retrospectively identified adult patients who underwent SEEG between January 2019 and May 2022. To study IED activation patterns, we classified IEDs as leading spikes (involved within the SOZ) and distant spikes (outside the SOZ). We calculated each patient\'s total number of brain subregions generating distant spikes. We correlated them with epilepsy type, duration, and surgical outcome (Engel I: good outcome and Engel II-IV: poor outcome). Results A total of 22 patients were identified during the study period, and 16 underwent surgical intervention (ablation or resection) with one-year post-surgery follow-up. The most common IED morphology was a single spike or sharp followed by periodic spikes or sharps. We found that 87% (n = 19/22) of leading spikes were activated during the first 24 hours of SEEG monitoring, whereas no activation pattern was observed for distant spikes. We found that a higher number of subregions generating distant spikes were associated with poor surgical outcomes (p = 0.002). However, we did not find any significant association between the number of subregions generating distant spikes with epilepsy duration (p = 0.67), temporal or extratemporal-onset epilepsy (p = 0.58), or the presence of an MRI lesion (p = 0.62). Conclusions IEDs involved within the SOZ were found to be activated during the first 24 hours of SEEG monitoring, which could aid in recognizing the pathological spikes and targeted mapping of the irritative zone. We also observed that a higher number of brain subregions generating IEDs outside the SOZ were associated with poor surgical outcomes, but this observation needs to be further studied with larger sample size prospective studies.