Search Results (412)

Assistance dogs have shown promise in alerting to epileptic seizures in their owners, but current approaches often lack consistency, standardisation, and objective validation. This proof-of-concept study presents the development and initial validation of a wearable behaviour-alert detection collar developed for trained assistance dogs. It demonstrates the technical feasibility for automated detection of trained signalling behaviours. The collar integrates an inertial sensor and machine learning pipeline to detect a specific, trained alert behaviour of two rapid clockwise spins used by dogs to signal a seizure event. Data were collected from six trained dogs, resulting in 135 labelled spin alerts. Although the dataset size is limited compared to other machine learning applications, this reflects the real-world constraint that it is not practical for assistance dogs to perform excessive spin signalling during their training. Four supervised machine learning models (Random Forest, Logistic Regression, Naïve Bayes, and SVM) were evaluated on segmented accelerometer and gyroscope data. Random Forest achieved the highest performance (F1-score = 0.65; accuracy = 92%) under a Leave-One-DOG-Out (LODO) protocol. The system represents a novel step toward combining intentional canine behaviours with wearable technology, aligning with trends on the Internet of Medical Things. This proof-of-concept demonstrates technical feasibility and provides a foundation for future development of real-time seizure-alerting systems, representing an important first step toward scalable animal-assisted healthcare innovation. Full article

Objectives: This study aims to evaluate the efficacy and safety of valproic acid (VPA) transition regimens (from intravenous to oral tablets) for anti-seizure treatment. Methods: A retrospective analysis was conducted on inpatients treated with intravenous VPA and oral tablets for epilepsy at the Sir Run Run Shaw Hospital, affiliated with Zhejiang University, between January 2022 and December 2023. Various transition strategies from VPA injections to tablets were examined, and the efficacy and safety of different transition strategies were analyzed. Results: A total of 164 inpatients receiving VPA transition therapy were included in this study, which was divided into three groups based on the transition timing: the 0 h group, the 0–48 h group, and the >48 h group. Regarding VPA dosage, the median daily dose of intravenous VPA was separately 1076.50 mg/day, 1200 mg/day and 1438 mg/day in the 0 h group, 0–48 h group, and the >48 h group. During transition, the daily doses of VPA were significantly higher than that before and after the transition. After completely switching to oral administration, they were all decreased to 1000 mg/day. Moreover, a significant difference regarding the clinical efficacy was observed among the three groups. The >48 h group showed the highest rate of clinical efficacy, which was significantly greater than that of the 0 h group and 0–48 h group. Although there was no statistical significance detected regarding the average blood serum concentrations among the three groups; notably, a higher proportion of patients in the >48 h group (19.35%) had blood concentrations exceeding the desired therapeutic window compared with the 0–48 h group (8.06%) and 0 h group (0%). Adverse events included 30 cases in the 0 h group, 42 in the 0–48 h group, and 67 in the >48 h group, with statistically significant differences in hemoglobin reduction, headache/dizziness, and liver injury. No significant differences were found in digestive and skin-related reactions. Conclusions: The results suggest that the >48 h transition regimen may show some advantages in efficacy but also increases the risk of adverse reactions significantly. Therefore, it is recommended to complete the intravenous-to-oral switch carefully with blood drug concentrations strictly monitored. Full article

Background/Objectives: New synthetic opioids (NSOs) like nitazenes pose significant public health risks due to their high potency and increasing prevalence. Ethyleneoxynitazene, a benzofuran-containing nitazene, recently emerged on the illicit market and was identified in seizures in Europe. Although no intoxications have been reported to date, its µ-opioid receptor activity raises concern. This study investigated the metabolism of ethyleneoxynitazene to better understand its pharmacological profile, toxicity, and detectability in clinical and forensic contexts. Methods: Ethyleneoxynitazene was incubated with cryopreserved human hepatocytes pooled from 10 donors. Metabolites were detected by liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) and identified using Compound Discoverer (Thermo Scientific; Waltham, MA, USA); detection and identification were assisted by in silico metabolite predictions with BioTransformer. Results: Sixteen metabolites were identified, with major biotransformations including N-deethylation at the N,N-diethylethanamine chain, hydroxylation at the dihydrofuran ring, and dihydrofuran ring opening via oxidative cleavage, leading to the formation of the corresponding ethanoic acid. Conclusions: This study provides the first characterization of the metabolism of a nitazene without an alkoxyphenyl moiety; the absence of this particular group reflects significant differences in the pharmacokinetic and pharmacodynamic profile compared to other nitazenes. We propose N-deethyl-3′-ethanoic acid-4′-hydroxy ethyleneoxynitazene, N-deethyl-hydroxy ethyleneoxynitazene, 3′-ethanoic acid-4′-hydroxy ethyleneoxynitazene, hydroxy ethyleneoxynitazene, and N-deethyl ethyleneoxynitazene as metabolite biomarkers of ethyleneoxynitazene consumption in clinical and forensic toxicology. Given the potential activity of some metabolites and interindividual variability in metabolic pathways, further studies are warranted to refine these findings through the analysis of biological samples from multiple ethyleneoxynitazene-positive cases. Full article

Alphaviruses are transmitted by Aedes mosquitoes and cause large-scale epidemics worldwide. Chikungunya virus (CHIKV) infection can cause febrile seizures known as chikungunya fever (CHIKF), which ultimately leads to severe joint pain and myalgia. While a vaccine has recently been introduced against CHIKV, at present, no anti-viral drug is available. CHIKV, like other alphaviruses, has a short 6K protein capable of forming an ion channel. Blocking this ion channel with drugs can therefore serve as a potential way to curtail CHIKV infection. To that end, we screened a repurposed drug library using three bacteria-based channel assays to detect blockers against 6K viroporin, yielding several hits. Interestingly, several of the blockers were able to inhibit the 6K protein from the similar Eastern equine encephalitis virus (EEEV), while others were not, pointing to structural specificity which may be explained by modeling studies. In conclusion, our study provides a starting point for developing a new route to potentially inhibit CHIKV. Full article

Autoimmune encephalitis (AE) is an autoantibody-mediated central nervous system disorder with diverse neuropsychiatric and neurological manifestations, and should be considered in the differential diagnosis of acute and subacute neurological or psychiatric syndromes. In this retrospective study, we analyzed 65 patients: 54 with AE (47 antibody-positive, seven antibody-negative) and 11 antibody-positive without AE. The most frequently detected antibodies targeted N-methyl-D-aspartate receptor (NMDAR), leucine-rich glioma-inactivated protein 1 (LGI1), and contactin-associated protein-like 2 (CASPR2)—key synaptic and axonal membrane proteins involved in excitatory neurotransmission, neuronal signaling, and synaptic plasticity. Clinical presentations were heterogeneous, ranging from common neuropsychiatric, cognitive, and seizure manifestations to atypical brainstem or cerebellar features. Symptom distribution analysis further demonstrated distinct patterns among Ab-positive AE, Ab-negative AE, and Ab-positive non-AE groups, with specific symptom–antibody associations providing potential diagnostic clues. Diagnostic complexity was underscored by unusual age at onset, overlap with multiple sclerosis, cases preceded by herpes labialis, and dual-antibody detection. A subset of antibody-positive patients had alternative diagnoses, highlighting the need for careful clinical correlation and cautious interpretation of antibody results. These findings illustrate the diagnostic challenges and broad clinical spectrum of AE, emphasizing the importance of integrating serological, clinical, and imaging data to improve diagnostic accuracy and guide management. Full article

Indoleamine 2,3-dioxygenase (IDO) activation by seizures elevates toxic tryptophan metabolites linked to seizure exacerbation. Brain-derived neurotrophic factor (BDNF)/tyrosine kinase B (TrkB) signaling, oxidative stress, and mitochondrial respiratory chain complex dysfunction contribute to temporal lobe epilepsy (TLE), but their regulatory links remain unclear. Male Kunming mice were grouped into Control, Control + 1-Methyl-DL-tryptophan (1-MT), TLE, and TLE + 1-MT. TLE was induced with 300 mg/kg pilocarpine. Two weeks after modeling, 1-MT (50 mg/kg) was administered twice daily for two weeks in 1-MT groups. Assessments included video monitoring to record seizure frequency and duration; Nissl and Fluoro-Jade B (FJB) staining to evaluate neuronal damage; real-time quantitative PCR (qRT-PCR) and Western blot to detect IDO, BDNF, and TrkB expression; assays for the following oxidative stress markers: malondialdehyde (MDA), glutathione (GSH), superoxide dismutase (SOD), catalase (CAT); and detection of mitochondrial complex I/IV activities. Results showed TLE mice had significantly increased IDO expression, BDNF/TrkB over-activation, elevated oxidative stress, impaired mitochondrial complex I/IV activities, severe neuronal damage, and increased seizure frequency/duration. 1-MT intervention reversed all these pathological changes, restoring levels to near-control status. This indicates IDO activation promotes TLE progression, which is associated with modulation of the BDNF/TrkB signaling pathway, exacerbation of oxidative stress, and impairment of mitochondrial complex I/IV activities—supporting IDO as a potential therapeutic target for TLE. Full article

Lennox–Gastaut syndrome (LGS) is a severe form of childhood-onset epilepsy, often associated with pharmacoresistance. As complete seizure control is usually not achievable with the use of drug therapy, non-pharmacological treatment may be offered to intractable patients. In this review, we are going to present literature reports on various non-pharmacological treatments, including surgical and dietary methods. Surgical interventions, such as resective surgery, corpus callosotomy (CC), or neuromodulation therapies such as vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS), can be offered to pharmacoresistant patients. If the epileptogenic area can be detected, resective surgery is a treatment of choice. On the contrary, if non-invasive and invasive diagnostic methods fail to detect epileptogenic lesions, CC and VNS are considered palliative surgical methods. While both CC and VNS are considered effective in seizure reduction, CC is still more popular than VNS, although VNS seems to be related to better tolerability. Although all neuromodulation therapies require multidirectional optimization, DBS appears to be particularly promising for LGS. The classic ketogenic diet (cKD) is considered an effective and well-tolerated method in LGS treatment. The modified Atkins diet (MAD) and the low glycemic index treatment (LGIT) could be used as valuable alternatives due to their lower restrictiveness and better tolerability. Moreover, combinations of several treatment methods could significantly improve LGS patients’ seizure outcomes. Full article

Objective: To evaluate the feasibility and effectiveness of wrist-worn wearable devices combined with machine learning (ML) approaches for detecting a diverse array of seizure types beyond generalized tonic–clonic (GTC), including focal, generalized, and subclinical seizures. Materials and Methods: Twenty-eight patients undergoing inpatient video-EEG monitoring at Mayo Clinic were concurrently monitored using Empatica E4 wrist-worn devices. These devices captured accelerometry, blood volume pulse, electrodermal activity, skin temperature, and heart rate. Seizures were annotated by neurologists. The data were preprocessed to experiment with various segment lengths (10 s and 60 s) and multiple feature sets. Three ML strategies, XGBoost, deep learning models (LSTM, CNN, Transformer), and ROCKET, were evaluated using leave-one-patient-out cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUROC), seizure-wise recall (SW-Recall), and false alarms per hour (FA/h). Results: Detection performance varied by seizure type and model. GTC seizures were detected most reliably (AUROC = 0.86, SW-Recall = 0.81, FA/h = 3.03). Hyperkinetic and tonic seizures showed high SW-Recall but also high FA/h. Subclinical and aware-dyscognitive seizures exhibited the lowest SW-Recall and highest FA/h. MultiROCKET and XGBoost performed best overall, though no single model was optimal for all seizure types. Longer segments (60 s) generally reduced FA/h. Feature set effectiveness varied, with multi-biosignal sets improving performance across seizure types. Conclusions: Wrist-worn wearables combined with ML can extend seizure detection beyond GTC seizures, though performance remains limited for non-motor types. Optimizing model selection, feature sets, and segment lengths, and minimizing false alarms, are key to clinical utility and real-world adoption. Full article

This study investigates on-edge seizure detection that aims to resolve two major constraints that hold the deployment of deep learning models in clinical settings at present. First, centralized training requires gathering and consolidating data across institutions, which poses a serious issue of privacy. Second, a high computational overhead inherent in inference imposes a crushing burden on resource-limited edge devices. Hence, we propose NeuroFed-LightTCN, a federated learning (FL) framework, incorporating a lightweight temporal convolutional network (TCN), designed for resource-efficient and privacy-preserving seizure detection. The proposed framework integrates depthwise separable convolutions, grouped with structured pruning to enhance efficiency, scalability, and performance. Furthermore, asynchronous aggregation is employed to mitigate training overhead. Empirical tests demonstrate that the network can be reduced fully to 70% with a 44.9% decrease in parameters (65.4 M down to 34.9 M and an inferencing latency of 56 ms) and still maintain 97.11% accuracy, a metric that outperforms both the non-FL and FL TCN optimizations. Ablation shows that asynchronous aggregation reduces training times by 3.6 to 18%, and pruning sustains performance even at extreme sparsity: an F1-score of 97.17% at a 70% pruning rate. Overall, the proposed NeuroFed-LightTCN addresses the trade-off between computational efficiency and model performance, delivering a viable solution to federated edge-device learning. Through the interaction of federated-optimization-driven approaches and lightweight architectural innovation, scalable and privacy-aware machine learning can be a practical reality, without compromising accuracy, and so its potential utility can be expanded to the real world. Full article

Introduction: Neuroinflammation is a common pathological hallmark of Coronavirus Disease 2019 (COVID-19) and epilepsy; however, their shared immunogenomic mechanisms remain poorly defined. This study explores shared immune-inflammatory transcriptomic signatures and identifies potential repositioning therapeutics. Methods: We integrated single-cell RNA-seq data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and healthy donors (GSE149689), and bulk RNA-seq data from hippocampal tissue of patients with Temporal Lobe Epilepsy with Hippocampal Sclerosis (TLE-HS) and healthy controls (GSE256068). Common Differentially Expressed Genes (DEGs) were identified and subjected to GO/KEGG enrichment, a PPI network, hub gene detection (cytoHubba), and transcriptional regulation analysis (ENCODE-based TF/miRNA networks). Drug repositioning was performed using the LINCS L1000 database. Results: We identified 25 DEGs shared across datasets, including 22 upregulated genes enriched in cytokine–cytokine receptor interaction, NF-κB, and Toll-like receptor pathways. PPI analysis revealed a CX3CR1–TLR4-centered immune module. Gabapentin emerged as a promising repositioning candidate with potential to downregulate CX3CR1, TLR4, and selectin P ligand (SELPLG). Receiver Operating Characteristic (ROC) analysis confirmed the diagnostic value of these targets (AUC > 0.90 in epilepsy). A mechanistic model was proposed to illustrate Gabapentin’s dual action on microglial polarization and cytokine suppression. Conclusions: Our results reveal a shared CX3CR1–TLR4–NF-κB inflammatory axis in COVID-19 and epilepsy, supporting Gabapentin as a potential dual-action immunomodulator. These findings reveal a previously underappreciated immunomodulatory role for Gabapentin, providing mechanistic rationale for its repositioning in neuroinflammatory conditions beyond seizure control. Full article

Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model’s ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection. Full article

The rustrela virus (RusV), a recently discovered member of the Matonaviridae family and a close relative of the rubella virus, has emerged as the etiological agent of “staggering disease”—a progressive neurological disorder primarily affecting domestic cats and other mammals. Characterized by nonsuppurative meningoencephalomyelitis, RusV infection manifests with clinical signs such as ataxia, seizures, and behavioral abnormalities. First identified in 2020, RusV has since been detected in various mammalian species across Europe and, more recently, in North America. This review provides a comprehensive summary of the current knowledge of RusV, including its taxonomy, genomic structure, host range, transmission hypotheses, clinical and histopathological features, and diagnostic challenges. Although the potential for zoonotic spillover has not yet been confirmed, it highlights the need for increased surveillance and further research. As an emerging neurotropic virus with potential for cross-species transmission, RusV may represent a significant concern for veterinary medicine and public health. Full article

Attraction of glioblastoma cells to potassium was suspected when glioblastoma cells clustered around dying cells and migrated towards serum (high [K⁺]) and increased potassium. Potassium channel proteins (KCN family, 90 members) mediating alterations in the transmembrane flux may provide K⁺ that releases H⁺ bound to inner membranes in cancer cells for cytosolic proton transfer, possibly conformational in water (Grotthuss), to extrusion sites. Cell settling and migration assay results led to collecting 70 studies, unbiased by the authors for inclusion of KCN genes, that detected KCN differentially expressed genes (DEGs). Of 53 KCN DEGs found among 29 malignancies, 62.3% encoded H⁺-sensitive proteins. KCN DEGs encoding H⁺-sensitive proteins were more prevalent in 50 studies involving one or more categories (seven oncogenes and histone/DNA modifiers) versus those with none; p = 0.0325. Pertinent genes for lactate outflow, etc., had relatively normal levels of expression. Brain tumors in REMBRANDT (database) showed altered expression of KCN genes encoding H⁺-sensitive proteins in glioblastomas versus less invasive oligodendrogliomas of patients on anti-seizure medications, with less KCNJ16/Kir5.1; p = 5.32 × 10⁻⁸ in glioblastomas. Altered H⁺-sensitive potassium flux via the KCN family, downstream of oncogenes and histone/DNA modifiers, putatively incites proton transfers for H⁺ release during pH reversal (pHi > pHe) in cancer. Full article

In addition to characteristic and easily identifiable behavioural signs—namely epileptic seizures—electroencephalography (EEG) has long been a standard component of epilepsy diagnosis protocols. In veterinary practice, EEG is typically performed in a semi-invasive manner, using subcutaneous electrodes and sedation. Here, we propose that the non-invasive polysomnography protocol, originally developed for basic research, can serve as a more welfare-friendly yet informative alternative for assessing epileptic brain activity in dogs. In this study, N = 11 family dogs diagnosed with epilepsy underwent a single non-invasive polysomnography session. EEG-based evidence for epileptic activity was detected in two cases. Polysomnography data from these 11 epileptic dogs were further analysed to evaluate sleep structure parameters. Compared to a matched control group of N = 11 clinically healthy dogs, the epileptic group exhibited reduced sleep efficiency, increased sleep latency, more wakings after sleep onset, and less time spent in drowsiness and non-REM sleep. These findings support the potential utility of non-invasive brain monitoring techniques, such as polysomnography, in the diagnosis and management of epilepsy in veterinary medicine. Full article

Background: Epilepsy is a chronic neurological disorder that affects individuals across all age groups. Early detection and intervention are crucial for minimizing both physical and psychological distress. However, the unpredictable nature of seizures presents considerable challenges for timely detection and accurate diagnosis. Method: To address the challenge of low recognition accuracy in small-sample, single-channel epileptic electroencephalogram (EEG) signals, this study proposes an automated seizure detection method using a micro-capsule network. First, we propose a dimensionality-increasing transformation technique for single-channel EEG signals to meet the network’s input requirements. Second, a streamlined micro-capsule network is designed by optimizing and simplifying the framework’s architecture. Finally, EEG features are encoded as feature vectors to better represent spatial hierarchical relationships between seizure patterns, enhancing the framework’s adaptability and improving detection accuracy. Result: Compared to existing EEG-based detection methods, our approach achieves higher accuracy on small-sample datasets while maintaining a reduction in computational complexity. Conclusions: By leveraging its micro-capsule network architecture, the framework demonstrates superior classification accuracy when analyzing single-channel epileptiform EEG signals, significantly outperforming both convolutional neural network-based implementations and established machine learning methodologies. Full article