Search Results (140)

Pyridoxine-dependent epilepsy (PDE) represents a group of rare developmental and epileptic encephalopathies. The most common PDE is caused by biallelic pathogenic variants in ALDH7A1 (PDE-ALDH7A1; OMIM #266100), which encodes α-aminoadipate semialdehyde (α-AASA) dehydrogenase, a key enzyme in lysine catabolism. Affected individuals present with seizures unresponsive to conventional anticonvulsant medications but responsive to high-dose of pyridoxine (vitamin B6). Adjunctive lysine restriction and arginine supplementation have also shown potential in improving neurodevelopmental outcomes. Given the significant benefit of early intervention, PDE-ALDH7A1 is a strong candidate for newborn screening (NBS). However, traditional biomarkers are biochemically unstable at room temperature (α-AASA and piperideine-6-carboxylate) or lack sufficient specificity (pipecolate), limiting their utility for biomarker-based NBS. The recent identification of two novel and stable biomarkers, 2S,6S-/2S,6R-oxopropylpiperidine-2-carboxylate (2-OPP) and 6-oxo-pipecolate (oxo-PIP), offers renewed potential for biochemical NBS. We evaluated the feasibility of incorporating 2-OPP, oxo-PIP, and pipecolate into routine butylated FIA-MS/MS workflows used for biochemical NBS. A total of 9402 dried blood spots (DBS), including nine confirmed PDE-ALDH7A1 patients and 9393 anonymized controls were analyzed using a single multiplex assay. 2-OPP emerged as the most sensitive biomarker, identifying all PDE-ALDH7A1 patients with 100% sensitivity and a positive predictive value (PPV) of 18.4% using a threshold above the 99.5th percentile. Combining elevated 2-OPP (above the 99.5th percentile) with either pipecolate or oxo-PIP (above the 85.0th percentile) as secondary marker detected within the same multiplex FIA-MS/MS assay further improved the PPVs to 60% and 45%, respectively, while maintaining compatibility with butanol-derivatized method. Notably, increasing the 2-OPP threshold above the 99.89th percentile, in combination with either pipecolate or oxo-PIP above the 85.0th percentile resulted in both 100% sensitivity and 100% PPV. This study supports the strong potential of 2-OPP-based neonatal screening for PDE-ALDH7A1 within existing NBS infrastructures. The ability to multiplex 2-OPP, pipecolate and oxo-PIP within a single assay offers a robust, practical, high-throughput and cost-effective approach. These results support the inclusion of PDE-ALDH7A1 in existing biochemical NBS panels. Further prospective studies in larger cohorts are needed to refine cutoffs and confirm clinical performance. Full article

Epilepsy is a chronic neurological disease with a relatively high incidence in the pediatric population. Anti-seizure medication (ASM) may cause adverse drug reactions (ADRs), which may occur repeatedly. Objective: This study aimed to analyze the recurrence of ADRs caused by ASMs over a period of 122 months in hospitalized Mexican pediatric epilepsy patients. The patients were under monotherapy or polytherapy treatment, with valproic acid (VPA), phenytoin (PHT), and levetiracetam (LEV), among others. A total of 313 patients met the inclusion criteria: 211 experienced ADRs, whereas 102 did not. Patient sex, age, seizure type, nutritional status and related drugs were considered explanatory variables. Methods: Four statistical models were used to analyze recurrent events that were defined as “one or more ADRs occurred on a single day”, considering both the classification of ADR seriousness and the ASM causing the ADR. Results: A total of 499 recurrence events were identified. The recurrence risk was significantly greater among younger patients for both nonsevere and severe ADRs and among those with focal seizures for nonsevere ADRs. Interestingly, malnutrition was negatively associated with the risk of nonsevere ADRs, and obesity was positively associated with the risk of severe ADRs. Finally, LEV was associated with a significantly greater risk of causing nonsevere ADRs than VPA. However, LEV significantly reduced the risk of severe ADRs compared with VPA, and PHT increased the risk in comparison with VPA. In conclusion, this study offers a robust clinical tool to predict risk factors for the presence and recurrence of ASM-ADRs in pediatric patients with epilepsy. Full article

Epilepsy remains a widespread neurological disorder, with approximately 30% of patients showing resistance to current antiepileptic therapies. To address this unmet need, a series of 2-(isoindolin-2-yl) esters derived from natural amino acids were designed and evaluated for their potential interaction with the GABA_A receptor. Sixteen derivatives were subjected to in silico assessments, including physicochemical and ADMET profiling, virtual screening–ensemble docking, and enhanced sampling molecular dynamics simulations (metadynamics calculations). Among these, compounds derived from the aromatic amino acids, phenylalanine, tyrosine, tryptophan, and histidine, exhibited superior predicted affinity, attributed to π–π stacking interactions at the benzodiazepine binding site of the GABA_A receptor. Based on computational performance, the tyrosine and tryptophan derivatives were synthesized and further assessed in vivo using the pentylenetetrazole-induced seizure model in zebrafish (Danio rerio). The tryptophan derivative produced comparable behavioral seizure reduction to the reference drug diazepam at the tested concentrations. The results implies that aromatic amino acid-derived isoindoline esters are promising anticonvulsant candidates and support the hypothesis that π–π interactions may play a critical role in modulating GABA_A receptor binding affinity. Full article

Hemolytic uremic syndrome (HUS), a thrombotic microangiopathy primarily affecting the kidneys, can also involve the central nervous system (CNS), often leading to significant morbidity and mortality. Neurologic manifestations are among the most severe extra-renal complications, particularly in children and during outbreaks of Shiga toxin-producing Escherichia coli (STEC)-associated HUS (typical (tHUS)). This review explores the clinical spectrum, pathophysiology, diagnostic workup, and age-specific outcomes of neurologic involvement in both typical (tHUS) and atypical (aHUS). Neurologic complications occur in up to 11% of pediatric and over 40% of adult STEC-HUS cases in outbreak settings. Presentations include seizures, encephalopathy, focal deficits, movement disorders, and posterior reversible encephalopathy syndrome (PRES). Magnetic resonance imaging (MRI) commonly reveals basal ganglia or parieto-occipital lesions, though subtle or delayed findings may occur. Laboratory workup typically confirms microangiopathic hemolytic anemia (MAHA), thrombocytopenia, and kidney damage, with additional markers of inflammation or metabolic dysregulation. Eculizumab is the first-line treatment for aHUS with CNS involvement, while its utility in STEC-HUS remains uncertain. Although many children recover fully, those with early CNS involvement are at greater risk of developing epilepsy, cognitive delays, or fine motor deficits. Adults may experience lingering neurocognitive symptoms despite apparent clinical recovery. Differences in presentation and imaging findings between age groups emphasize the need for tailored diagnostic and therapeutic strategies. Comprehensive neurorehabilitation and long-term follow-up are crucial for identifying residual deficits. Continued research into predictive biomarkers, neuroprotective interventions, and standardized treatment protocols is needed for improving outcomes in HUS patients with neurological complications. Full article

The current study evaluated the anticonvulsant properties of ethanolic extracts from Morus alba, Angelica archangelica, Passiflora incarnata, and Valeriana officinalis using integrated phytochemical, in vivo, biochemical, and computational approaches. Phytochemical analysis by UHPLC-HRMS/MS revealed the presence of various bioactive compounds, notably flavonoids such as isorhamnetin, quercetin, and kaempferol. In an electroshock-induced seizure model, Morus alba extract (MAE, 100 mg/kg) demonstrated significant anticonvulsant effects, reducing both seizure duration and incidence, likely mediated by flavonoid interactions with GABA-A and 5-HT3A receptors, as suggested by target prediction and molecular docking analyses. The extracts of Angelica archangelica (AAE, 100 mg/kg) and Passiflora incarnata (PIE, 50 mg/kg) exhibited moderate, non-significant anticonvulsant activities. At the same time, Valeriana officinalis (VOE, 50 mg/kg) displayed considerable antioxidant and anti-inflammatory properties but limited seizure protection. All extracts significantly reduced brain inflammation markers (TNF-α) and enhanced antioxidant defenses, as indicated by total thiols. Molecular docking further supported the interaction of key phytochemicals, including naringenin and chlorogenic acid, with human and mouse 5-HT3A receptors. Overall, Morus alba extract exhibited promising therapeutic potential for epilepsy management, warranting further investigation into chronic seizure models and optimized dosing strategies. Full article

Background: There is growing evidence that neuroinflammation is involved in epileptogenesis. Identifying its biomarkers can be important for distinguishing epilepsy patients from healthy individuals and differentiating well-controlled epilepsy from drug-resistant epilepsy (DRE). Methods: An observational case-control study at Malaga’s Regional University Hospital involved epilepsy patients divided into three groups: healthy controls (HC), seizure-free epilepsy (SFE), and DRE. Demographic and clinical data and plasmatic and/or CSF levels of 24 different inflammation-related molecules were collected for each patient and were analyzed through univariate and multivariate analysis. Results: The study included 68 patients: 38 in the DRE group, 14 in the SFE group, and 16 in the HC group. A new Inflammatory Drug-Resistant Epilepsy Index (IDREI) was created using key variables with significant or trending significance. This index combined pro-inflammatory mediators (ICAM-1 and NfL) and anti-inflammatory factors (IL-10 and IL-4), showing statistical significance (p = 0.002). ROC curve analysis for the IDREI gave an AUC of 0.731 (95% CI: 0.608–0.854). A multivariate logistic regression model’s ROC analysis resulted in a higher AUC of 0.891 (95% CI: 0.791–0.991). Conclusions: The IDREI molecular index shows promise in predicting epilepsy and drug-resistant epilepsy (DRE). Additional prospective studies are required to assess its clinical utility. Full article

Artificial intelligence (AI) has emerged as a transformative tool in the analysis and management of epilepsy through its integration with electroencephalography (EEG) data. The adoption of AI-assisted solutions in managing epilepsy holds the potential to significantly enhance the efficiency and accuracy for diagnosing this complex condition. However, AI-assisted EEG technologies are infrequently adopted in clinical settings. In this Review, we provide an overview of AI applications in seizure prediction, detection, syndrome classification, surgical planning, and prognosis prediction. Additionally, we explore the methodological considerations and challenges that are relevant in clinical settings. Overall, AI has the potential to revolutionize epilepsy management, ultimately improving patient outcomes and advancing the field of precision medicine. Fostering interdisciplinary collaborations between AI researchers, neurologists, and ethicists will be crucial in creating integrated solutions that address both technical and clinical requirements. Full article

Epilepsy is widely known as a network disease. Ictal and interictal activities are generated and spread within the existing networks involving different regions of the brain. Network alterations affect both grey and white matter, deep brain nuclei, including those of the ascending reticular formation. These structures may be involved in a disorganized connectome associated with epilepsy. A growing body of neuroimaging and neuropsychological findings suggests that global and focal network aberrations are closely linked to cognitive deficits in epilepsy patients. This evidence relates equally to focal epilepsies, such as temporal lobe epilepsy or extra-temporal lobe epilepsy, as well as generalized epilepsies, such as juvenile myoclonic epilepsy. Network abnormalities have been associated with a broad range of cognitive impairments, including language, memory, and executive functions, as well as sensory and motor functions. Whole-brain structural connectome models help in the understanding of seizure generation and spread. Identifying key nodes of seizure propagation may help in planning surgical procedures in individual patients by simulating epilepsy surgery on virtual models. Functional connectomic profiles may predict seizure outcomes in patients who undergo deep brain stimulation due to intractable seizures. Therefore, individualized interventional strategies could be developed based on connectome characteristics. Full article

Epilepsy is a prevalent neurological condition, having a wide range of phenotypic traits, which complicate the diagnosis process. Next-generation sequencing (NGS) techniques have improved the diagnostics for unexplained epilepsies. Our goal was to evaluate the utility and impact of genetic testing in the clinical management of pediatric epilepsies. In addition, we aimed to identify clinical factors that could predict a genetic diagnosis. This was a retrospective study of 140 pediatric patients with epilepsy with or without other neurological conditions that underwent NGS testing (multigene panel, WES = whole exome sequencing and/or WGS = whole genome sequencing). A comparison between genetically diagnosed versus non-diagnosed children was performed based on different clinical features. Univariate and multivariate logistic regression analysis was performed to identify clinical predictors of a positive genetic diagnosis. Most children underwent gene panel testing, while 30 had exome sequencing and 3 had genome sequencing. The overall diagnostic yield of genetic testing was 28.6% (40/140) for more than 28 genes. The most frequently identified genes with causative variants were SCN1A (n = 4), SCN2A (n = 3), STXBP1 (n = 3), MECP2 (n = 2), KCNQ2 (n = 2), PRRT2 (n = 2), and NEXMIF (n = 2). Significant predictors from the logistic regression model were a younger age at seizure onset (p = 0.015), the presence of intellectual disability (p = 0.021), and facial dysmorphism (p = 0.049). A genetic diagnosis led to an impact on the choice or duration of medication in 85% (34/40) of the children, as well as the recommendation for screening of comorbidities or multidisciplinary referrals in 45% (18/40) of children. Epilepsy is a highly heterogeneous disorder, both genetically and phenotypically. Less than one third of patients had a genetic diagnosis identified using panels, exomes, and/or genomes. An early onset and syndromic features (including global developmental delay) were more likely to receive a diagnosis and benefit from optimized disease management. Full article

Background/Objectives: For patients with medically refractory temporal lobe epilepsy (TLE), surgery is an effective strategy. However, post-operative seizure recurrence occurs in 20–30% of patients, and it remains challenging to predict outcomes solely based on clinical variables. Here, we ask to what extent differences in gene expression in epileptic tissue can predict the outcome after resective epilepsy surgery. Methods: We performed RNAseq on hippocampal tissue resected from eight patients who underwent anterior temporal lobectomy with amygalohippocampectomy (ATL/AH), half of whom became seizure free (SF) or non-seizure free (NSF). Results: Bioinformatic analyses revealed 1548 differentially expressed genes and statistical enrichment analyses identified a distinct set of pathways in NSF and SF cohorts that were associated with neuroinflammation, neurotransmission, synaptic plasticity, and extracellular matrix (ECM) reorganization. Resected tissue exhibiting strong pro-inflammatory processes are associated with better post-surgery seizure outcomes than patients exhibiting cellular signaling processes related to ECM reorganization, autoantibody production, and neural circuit formation. Conclusions: The results suggest that post-operative targeting of both inhibitory aspects of the ECM remodeling and the autoimmune/inflammatory components may be helpful in promoting repair and preventing the recurrence of seizures. Full article

Background: In deep learning-based epilepsy prediction and classification, enhancing the extraction of electroencephalogram (EEG) features is crucial for improving model accuracy. Traditional supervised learning methods rely on large, detailed annotated datasets, limiting the feasibility of large-scale training. Recently, self-supervised learning approaches using masking-and-reconstruction strategies have emerged, reducing dependence on labeled data. However, these methods are vulnerable to inherent noise and signal degradation in EEG data, which diminishes feature extraction robustness and overall model performance. Methods: In this study, we proposed a self-supervised learning Transformer network enhanced with Adaptive Frequency-Time Attention (AFTA) for learning robust EEG feature representations from unlabeled data, utilizing a masking-and-reconstruction framework. Specifically, we pretrained the Transformer network using a self-supervised learning approach, and subsequently fine-tuned the pretrained model for downstream tasks like seizure prediction and classification. To mitigate the impact of inherent noise in EEG signals and enhance feature extraction capabilities, we incorporated AFTA into the Transformer architecture. AFTA incorporates an Adaptive Frequency Filtering Module (AFFM) to perform adaptive global and local filtering in the frequency domain. This module was then integrated with temporal attention mechanisms, enhancing the model’s self-supervised learning capabilities. Result: Our method achieved exceptional performance in EEG analysis tasks. Our method consistently outperformed state-of-the-art approaches across TUSZ, TUAB, and TUEV datasets, achieving the highest AUROC ($0.891$), balanced accuracy ($0.8002$), weighted F1-score ($0.8038$), and Cohen’s kappa ($0.6089$). These results validate its robustness, generalization, and effectiveness in seizure detection and classification tasks on diverse EEG datasets. Full article

Background: Autism spectrum disorders (ASDs) comprise a neurodevelopmental disease marked by impaired social communication and repetitive activities. An imbalance between excitatory and inhibitory neurotransmitters, such as glutamate and GABA, may play a significant function in ASDs. The neurophysiological process behind epilepsy is abnormal neuronal excitatory firing in particular brain regions brought on by a lack of GABAergic inhibition. The study of GABAergic dysfunction could explain the substantial comorbidity with epilepsy or increased susceptibility to seizures observed in people with autism. Objective: This study analyzes molecular indicators directly and indirectly related to GABAergic inhibitory signaling in individuals with autism and healthy controls, with the purpose of uncovering probable diagnoses. Methods: The study included 46 male autistic participants and 26 age- and gender-matched healthy controls. Plasma levels of two chloride co-transporters (KCC2, NKCC1), and vitamin D3 were evaluated using ELISA. Results: Autistic individuals showed a significant drop in all three examined variables when compared to healthy controls. Statistical methods such as correlation, combined receiver operating characteristic (ROC) curve analysis, and multiple regression modeling were used to assess the diagnostic value and interrelationships of these biomarkers. A significant increase in the area under the curve was seen using the combined ROC curve analysis. The combined variables also exhibited significantly higher sensitivity and specificity as an index of high predictiveness values. Measurement of plasma levels of vitamin D status and chloride co-transporters (KCC2, NKCC1) in children with ASD may help to better understand how sleep disturbances and epilepsy as comorbidities of ASD linked to vitamin D deficiency and peculiar inhibitory/excitatory effects of GABA. Full article

Near-Infrared Spectroscopy (NIRS) is a non-invasive technique that measures the oxygenation variations of brain tissue in response to different stimuli. It has many advantages such as being easy to use, portable, and non-invasive. Several studies over the years have demonstrated the usefulness of NIRS in neurological and neurodegenerative diseases. NIRS remains relatively underutilized in clinical practice. The aim of this brief review was to describe the use of NIRS in neurological and neurodegenerative diseases and how its use can modify clinical, therapeutic, and rehabilitative approaches. A total of 54 relevant articles were selected from the PUBMED research database related to the diagnostic and prognostic role of fNIRS in the main neurological and neurodegenerative diseases; significant outcomes have been reported in a descriptive form with careful considerations. In addition, we excluded studies using fNIRS in co-registration with other neurophysiological techniques. The use of NIRS should be applied even in the field of neurological and neurodegenerative diseases; in dementia, NIRS can aid in differential diagnosis and predict possible evolutions from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) stage; in stroke, it plays an important role especially in the post-acute phase, giving information about the patient’s chances of recovery; in Parkinson’s Disease (PD), the results showed the important role of cognitive aspects; in epilepsy, NIRS can localize the epileptic focus or potentially predict seizure onset. Full article

Background/Objectives: Epilepsy is a common chronic and recurrent neurological disorder that poses a threat to human health, and Acorus tatarinowii Schott (ATS), a traditional Chinese medicine, is used to treat it. This study aimed to determine its effects on plasma metabolites. Moreover, the possible mechanism of its intervention in epilepsy was preliminarily explored, combined with network pharmacology. Methods: An epileptic model of rats was established using pentylenetetrazol. The potential targets and pathways of ATS were predicted by network pharmacology. Ultra Performance Liquid Chromatography–Quadrupole–Time of Flight Mass Spectrometrynce Liquid Chromatography–Quadrupole–Time of Flight Mass Spectrometryance Liquid Chromatography–Quadrupole–Time of Flight Mass Spectrometry and statistical analyses were used to profile plasma metabolites and identify ATS’s effects on epilepsy. Results: Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that ATS was involved in regulating multiple signaling pathways, mainly including the neuroactive ligand–receptor interaction and GABAerGamma-aminobutyrate transaminaseAminobutyrate Transaminaseapse signaling pathway. ATS treatment restored 19 metabolites in epiGamma-aminobutyrate transaminaseminobutyrate Transaminase rats, affecting lysine, histidine, and purine metabolism. GABA-T was found as a new key target for treating epilepsy with ATS. The IC₅₀ of ATS for inhibiting GABA-T activity was 57.9 μg/mL. Through metabolomic analysis, we detected changes in the levels of certain metabolites related to the GABAergic system. These metabolite changes can be correlated with the targets and pathways predicted by network pharmacology. One of the limitations of this study is that the correlation analysis between altered metabolites and seizure severity remains unfinished, which restricts a more in-depth exploration of the underlying biological mechanisms. In the future, our research will focus on conducting a more in-depth exploration of the correlation analysis between altered metabolites and seizure severity. Conclusions: These results improved our understanding of epilepsy and ATS treatment, potentially leading to better therapies. The identification of key metabolites and their associated pathways in this study offers potential novel therapeutic targets for epilepsy. By modulating these metabolites, future therapies could be designed to better manage the disorder. Moreover, the insights from network pharmacology can guide the development of more effective antiepileptic drugs, paving the way for improved clinical outcomes for patients. Full article

Background/Objectives: Temporal lobe epilepsy (TLE) often develops following an initial brain injury, where specific triggers lead to epileptogenesis—a process transforming a healthy brain into one prone to spontaneous, recurrent seizures. Although electroencephalography (EEG) remains the primary diagnostic tool for epilepsy, it cannot predict the risk of epilepsy after brain injury. This limitation highlights the need for biomarkers, particularly those measurable in peripheral samples, to assess epilepsy risk. This study investigated urinary metabolites in a rat model of TLE to identify biomarkers that track epileptogenesis progression across the acute, latent, and chronic phases and elucidate the underlying mechanisms. Methods: Status epilepticus (SE) was induced in rats using repeated intraperitoneal injections of lithium chloride–pilocarpine hydrochloride. Urine samples were collected 48 h, 1 week, and 6 weeks after SE induction. Nuclear magnetic resonance spectrometry was used for metabolomic analysis, and statistical evaluations were performed using MetaboAnalyst 6.0. Differences between epileptic and control groups were represented using the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Volcano plot analysis identified key metabolic changes, applying a fold-change threshold of 1.5 and a p-value < 0.05. Results: The acute phase exhibited elevated levels of acetic acid, dihydrothymine, thymol, and trimethylamine, whereas glycolysis and tricarboxylic acid cycle metabolites, including pyruvic and citric acids, were reduced. Both the acute and latent phases showed decreased theobromine, taurine, and allantoin levels, with elevated 1-methylhistidine in the latent phase. The chronic phase exhibited reductions in pimelic acid, tiglylglycine, D-lactose, and xanthurenic acid levels. Conclusions: These findings highlight stage-specific urinary metabolic changes in TLE, suggesting distinct metabolites as biomarkers for epileptogenesis and offering insights into the mechanisms underlying SE progression. Full article