BACKGROUND: Anti-IgLON5 encephalitis was a rare neurological and heterogeneous disorder, which was mainly found in adults. Epileptic seizures related to anti-IgLON5 disease were rarely reported.
METHODS: Neural antibodies associated with autoimmune encephalitis in serum and cerebrospinal fluid (CSF) were tested using cell-based assays (CBA) with immunofluorescence double staining. The antibodies in serum were further confirmed by tissue-based assay (TBA) with rat brain and kidney tissue.
RESULTS: We reported a pediatric case presented with epileptic seizures, cognitive impairments, and sleep disorders. Autoantibody screening showed anti-IgLON5 antibody IgG (1:100+) and anti-NMDAR antibody IgG (1:10+) in the serum. She was diagnosed as anti-IgLON5 encephalitis. Her conditions improved rapidly by treated with intravenous immunoglobulin and high dose intravenous methylprednisolone.
CONCLUSIONS: We described the second pediatric case with anti-IgLON5 encephalitis, who was also the first presented with epileptic seizures as the initial presentation. Anti-IgLON5 encephalitis might have mild manifestations. For patients with new onset seizures associated with cognitive impairments and sleep disturbances, anti-IgLON5 antibody should be tested as early, even in children.

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.

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.

To validate the different predictive scoring scales in the Chinese population with new-onset epileptic seizures or epilepsy of unknown etiology related to neuronal surface antibody (Ab)-mediated autoimmune encephalitis (AE).
We retrospectively reviewed the charts of 174 consecutive patients from October 2018 to December 2022, whose serum and cerebrospinal fluid samples were tested for neuronal surface Abs. The antibody prevalence in epilepsy and encephalopathy (APE2), antibodies contributing to focal epilepsy signs and symptoms (ACES), \"obvious\" indications for neural antibody testing in epilepsy or seizures (ONES) checklist, and the combinations were used to validate the predictive models of neuronal surface Ab-mediated AE.
A total of 139 patients with new-onset epileptic seizures or epilepsy of unknown etiology were enrolled. Abs were detected in 37 patients (26.6%). The APE2/ONES reflex score had the highest sensitivity (89.2%) and lowest specificity (41.7%). The ACES score had the lowest sensitivity (67.5%) and highest specificity (64.7%). Variations in the performance were observed in the different types of AE. 100% of patients with anti-γ-aminobutyric acid B-B receptor encephalitis were predicted by ONES, APE2/ONES reflex, and ACES/ONES reflex scores. Only 75% of patients with anti-N-methyl-D-aspartate receptor encephalitis were predicted by the APE2/ONES and ACES/ONES reflex scores.
Our study was the first to validate various predictive scoring scales in the Chinese cohort of patients with new-onset epileptic seizures or epilepsy of unknown etiology related to neuronal surface Ab-mediated AE. Based upon clinical suspicion, more than one scoring scale should be performed to predict the chance of AE in those patients.

We present the case of a young male patient experiencing a transient loss of consciousness and manifesting a seizure when he tilted his head backward. Transcranial Doppler ultrasound (TCD) and carotid artery ultrasound (CAU) examination were normal when the patient\'s neck was in the neutral position. However, the CAU revealed vertebral artery (VA) transient occlusion during neck rotation or backward movement. Electroencephalogram (EEG) monitoring was performed with multiple neck rotation-induced tests. The patient developed dizziness, which was the same as the prodromal symptoms of the first seizure, and the EEG showed a large number of spinal slow waves and sharp slow waves in the frontal-to-frontal midline area, with an occasional generalization trend. CT angiography revealed occipitalization of the atlas and the lack of contrast agent filling in the local area of the VA when the patient\'s head was turned contralaterally. Thus, the patient was diagnosed with Bow Hunter\'s syndrome (BHS) and treated conservatively with neck immobilization. No recurrence occurred at 3 and 6 months of follow-up. Therefore, this case alerts neurologists to suspect BHS on observing seizure manifestations during neck rotation, and CAU may be a recommended dynamic screening method for BHS. This report is accompanied by a discussion of the phenomenon and diagnosis in the context of the existing literature.

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram\'s (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model\'s efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques.

Disruption of gamma-amino butyric acid type A receptors (GABAA Rs) synaptic clustering and a decrease in the number of GABAA Rs in the plasma membrane are thought to contribute to alteration of the balance between excitatory and inhibitory neurotransmission, which promotes seizure induction and propagation. The multipass transmembrane protein cleft lip and palate transmembrane protein 1 (Clptm1) controls the forward trafficking of GABAA R, thus decaying miniature inhibitory postsynaptic current (mIPSC) of inhibitory synapses. In this study, using a pentylenetetrazol (PTZ)-induced epilepsy rat model, we found that Clptm1 regulates epileptic seizures by modulating GABAA R-mediated inhibitory synaptic transmission. First, we showed that Clptm1 expression was elevated in the PTZ-induced epileptic rats. Subsequently, we found that downregulation of Clptm1 expression protected against PTZ-induced seizures, which was attributed to an increase in the number of GABAA Rγ2s in the plasma membrane and the amplitude of mIPSC. Taken together, our findings identify a new anti-seizure target that provides a theoretical basis for the development of novel strategies for the prevention and treatment of epilepsy.

Anti-IgLON5 is a rare neurologic disease that can present with epileptic seizures. However, epileptic seizures have not been characterized and are underreported. We aimed to investigate the clinical characteristics and demographics of epileptic seizures in patients with anti-IgLON5 disease.
We reported a case of anti-IgLON5 disease presenting with epileptic seizures and presented a comprehensive literature review on epileptic seizures in patients with the anti-IgLON5 disease. We searched the Medline, Pubmed, and Web of Science databases using the following search algorithm: \"IgLON5\" or \"anti-IgLON5\" or \"IgLON5 antibody\" limited to publications in English.
We identified 183 cases from 66 publications. In addition to our case, nine (4.9%) patients with anti-IgLON5 disease had reported epileptic seizures, either focal or generalized. Of those, epileptic seizures were one of the main reasons for neurology consultation in six (3.2%). Patients with epileptic seizures affected both sexes similarly and usually developed in middle age. In addition to epileptic seizures, a majority of patients had sleep disorders and cognitive impairment. The frequency of epileptic seizures was reduced with the treatment of immunotherapy and antiseizure medication.
Anti-IgLON5 disease can present with epileptic seizures, and our study expands the clinical spectrum of the anti-IgLON5 disease.