Ictal Events Research Articles

Clinical features and outcomes of late-onset epilepsy of unknown etiology: A retrospective study in West China.

Late-onset epilepsy (LOE) usually refers to the development of epilepsy at the age of 50years or older. Approximately 20% of LOE cases are diagnosed as late-onset epilepsy of unknown etiology (LOUE) due to a lack of an identifiable cause. The aim of this study was to investigate the clinical features, seizure and cognitive outcomes of patients with LOUE in West China. Patients diagnosed with LOUE at West China Hospital between January 2015 and December 2022 were retrospectively recruited. The seizure and cognitive outcome were followed up for at least 1year after discharge. Logistic regression models were applied to investigate the risk factors of recurrent seizure and cognitive impairment in patients with LOUE. We included 286 LOUE patients with a median seizure onset age of 59years. The most common seizure types were focal to bilateral tonic-clonic seizure (61.9%) and focal non-motor seizure (37.0%). Two-hundred and seventy-seven (96.9%) patients underwent video electroencephalography (VEEG), with seizures recorded in 11.9% of patients and interictal epileptiform discharges in 58.2% cases. Majority of the patients (73.4%) received monotherapy, with levetiracetam, oxcarbazepine and valproate being the most commonly prescribed anti-seizure medications. During the follow-up, 69.1% of patients achieved seizure-free. Multivariate analysis identified ictal event recorded during VEEG monitoring (OR:0.205, 95% CI: 0.045-0.932, p=0.040) and memory impairment (OR:2. 470, 95% CI: 1.181-5.167, p=0.016) as significant factors associated with recurrent seizure. Twenty-two patients were classified as cognitive impairment. The onset age (OR:1.095, 95% CI:1.032-1.162, p=0.003) and total Fazekas score (OR=6.770, 95% CI:1.972-23.241, p=0.002) were significant risk factors associated with cognitive dysfunction. LOUE is generally a benign form of epilepsy with a high percentage of patients achieving seizure-free status. However, these patients are at a higher risk of memory decline and cognitive dysfunction.

Retrospective discrimination of PNES and epileptic seizure types using blood RNA signatures

ObjectivesThe ability to differentiate epileptic- and non-epileptic events is challenging due to a lack of reliable molecular seizure biomarker that provide a retrospective diagnosis. Here, we use next generation sequencing methods on whole blood samples to identify changes in RNA expression following seizures.MethodsBlood samples were obtained from 32 patients undergoing video electroencephalogram (vEEG) monitoring. Blood samples were collected in PaxGene tubes at baseline (admission) and following a seizure event (4–6 h and 24 h later or discharge). EEG and video of clinical events were reviewed by the clinical team and study epileptologist and were classified as epileptic seizure, psychogenic nonepileptic spell (PNES), or other. RNA was extracted from blood and RNA expression was determined using RNA-sequencing.ResultsWe show significant differences in RNA profiles between patients that did or did not experience an epileptic seizure. Compared to baseline patients with PNES show large increases in RNA expression 4–6 h and 24 h post seizure. Conversely, genes that changed following epileptic seizure showed more modest changes associated with a decrease in immune system function. Transcript usage was changed between patients with PNES and epileptic seizure at all three time points examined. Lists of genes differentially expressed following PNES or epileptic seizure vs. all baseline samples were used as classifiers for prediction. Models generated using random forest and radial support vector machine algorithms were 100% accurate at predicting both PNES and epileptic seizures.SignificanceThese data suggest that blood gene expression changes may have utility to retrospectively identify patients who have suffered a seizure or seizure-like event as a cause of transient loss of consciousness.

High-field MRI findings in epileptic dogs with a normal inter-ictal neurological examination.

Epilepsy is one of the most common chronic neurological conditions affecting dogs. Previous research exploring the likelihood of a structural cause of epilepsy specifically in dogs with a normal inter-ictal examination is limited to a small population of dogs using low-field MRI. The aims of this study were to establish high-field (1.0T and 1.5T) MRI findings in dogs presenting with epileptic seizures and a normal inter-ictal examination. Medical records were retrospectively searched for dogs presenting with at least two epileptic seizure events more than 24 h apart. To be included in the study, patients had to have a normal neurological examination, high-field MRI of the brain and have had metabolic and toxic causes excluded. Four hundred and twelve dogs were eligible for inclusion. Crossbreeds were most commonly affected (n = 63, 15.3%) followed by Border collies (n = 39, 9.5%) and Labrador retrievers (n = 26, 6.3%). Seventy-six dogs (18.5%) had abnormalities detected on MRI, 60 (78.9%) of which were considered to be incidental. Overall, 16 dogs (3.9%) had a structural cause of their epileptic seizures including neoplasia (n = 13, 81.3%), anomalous (n = 2, 12.5%) and meningoencephalitis of unknown origin (MUO) (n = 1, 6.3%). When split into age group at first epileptic seizure structural lesions were documented in 0/66 dogs aged <1 year, 4/256 (1.6%) dogs aged ≥1 year ≤6 years (three neoplastic and one anomalous), 3/51 (5.9%) aged >6 years ≤8 years (two neoplastic and one MUO), and 9/39 (23.1%) dogs aged >8 years (eight neoplastic, one anomalous). Multivariate analysis identified two risk factors for structural disease: increasing age at first epileptic seizure (p < 0.001, OR = 4.390, CI 2.338-8.072) and a history of status epilepticus (p = 0.049, OR = 4.389, CI 1.010-19.078). Structural lesions are an uncommon cause of epilepsy at any age in dogs with a normal inter-ictal examination.

Safety of hyperbaric oxygen therapy in non-emergent patients with a history of seizures: A retrospective cohort study.

Hyperbaric oxygen therapy (HBOT) is well established as a treatment for various medical conditions. However, it poses a risk of oxygen toxicity, which can cause seizures particularly in individuals with pre-existing seizure disorders. Consequently, seizure disorders are considered a relative contraindication to HBOT. Despite this, the relative risk of HBOT-induced seizures in this patient population remains unclear. This retrospective cohort study aims to evaluate the safety of HBOT among patients with pre-existing seizure disorders. After obtaining approval from the Research Ethics Board, we retrospectively reviewed the patient charts of individuals with a history of seizures who were referred to the Rouge Valley Hyperbaric Medical Center and Toronto General Hyperbaric Medicine Unit for HBOT between January 2020 and December 2023. Relevant demographic information, past medical history, and HBOT session treatment protocols, such as the treatment pressure set in absolute atmospheric pressure (ATA) and number of air breaks, were recorded. The collected data was analyzed using descriptive statistics. A total of 43 patients were referred to HBOT during the study period, and 21 patients did not proceed with the treatments. In total, 634 HBOT sessions were administered to 22 patients in monoplace chambers with five-minute air breaks, and one patient experienced a seizure event. Each patient completed an average of 29 (range 3-60) sessions lasting 90-120 minutes at 1.8 ATA (n = 3), 2.0 ATA (n = 18), or 2.4 ATA (n = 1). Fifteen patients were on oral antiseizure medications during the HBOT course. The overall incidence of seizures was one in 634 treatments. While patients with a history of seizures may develop seizure activity during HBOT, the majority can safely undergo treatment when predetermined protocols are followed. With careful management and adherence to established protocols, HBOT can be a viable treatment option for those with seizure histories.

Seizure Risk Factors and Management Approaches in Patients with Brain Metastases

Abstract A significant proportion of patients with brain metastases experience a seizure event during their disease course, which can impact morbidity and long-term outcomes. A host of factors elevate the risk for seizures in patients with brain metastases, including patient factors, metabolic imbalances, tumor burden, and treatment modality. While reducing tumor burden via local and systemic therapies remains a critical component to mitigating seizure events, select patients may remain at risk. The use of prophylactic anti-seizure medications may be warranted in a subset of patients, though several clinical trials and guidelines from medical societies currently recommend against prophylactic use. Variability in the use of prophylactic anti-seizure medications in clinical practice underscores the need to update our current understanding of seizure risk in the era of multi-modality treatment and to identify opportunities to improve risk stratification and management. Herein, we provide a comprehensive literature review summarizing the current standard for seizure management in patients with brain metastases and assess the impact of multi-modal therapies on seizure risk. We additionally highlight gaps in the literature and present opportunities for future investigation.

Automated validated tool for epileptic seizure detection using deep learning

This paper explores an innovative approach for the automatic detection of epileptic seizures from audio recordings and Heart Rate Variability (HRV) using Convolutional Neural Networks (CNNs). In medical settings, accurately labeling seizure events is critical for patient monitoring. However, manual annotation by experts is not only time-intensive but also highly repetitive. To address this challenge, we developed a structured questionnaire for patients and eyewitnesses, concentrating on observable characteristics during typical seizure events. This questionnaire was used to prospectively study 198 consecutive adult patients with either Psychogenic Non-Epileptic Seizures (PNES) or Epileptic Seizures (ES). For each question, specific signs, symptoms, and risk factors were extracted as variables. The results showed a sensitivity of 95.10% and a specificity of 97.06%, confirming the reliability of the questionnaire. Also, the method proposed in the study categorizes all seizure vocalizations into a singular target event class, modeling the detection task as a binary classification problem target (seizure event) vs. non-target (non-seizure event). The CNN is trained to detect seizure events in short time frames. Experimental results indicate that the method achieves over 92.5% detection accuracy. Furthermore, the research leverages the correlation between pre-ictal epileptic states and HRV features. By addressing the noise interference commonly present during seizures, the proposed model can robustly train the CNN to identify pre-ictal states. The model's performance is promising, yielding an accuracy of over 91.5% for both positive and negative predictions. The proposed system underwent a human evaluation by a group of physicians at Mansoura University Hospital. The results were highly satisfactory, with the doctors expressing strong approval of the system's performance.

Time series segmentation for recognition of epileptiform patterns recorded via microelectrode arrays in vitro.

Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Approximately 30-40% of patients respond poorly to antiepileptic medications, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy. To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. In this regard, the detection and classification of events from ongoing brain activity, while achieving low power consumption through computationally inexpensive implementations, represents a major challenge in the field. To address this challenge, we here present two algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing time series segmentation (TSS), which involves extracting different levels of information from the LFP and relevant events from it. The algorithms were validated validated against epileptiform activity induced by 4-aminopyridine in mouse hippocampus-cortex (CTX) slices and recorded via microelectrode array, as a case study. The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. While initially trained specifically for detecting ictal activity, these algorithms can be fine-tuned for improved interictal detection, aiming at seizure prediction. Our results suggest that these algorithms can effectively capture epileptiform activity, supporting seizure detection and, possibly, seizure prediction and control. This opens the opportunity to design new algorithms based on this approach for closed-loop stimulation devices using more elaborate decisions and more accurate clinical guidelines.

A high seizure burden increases brain concentrations of specialized pro-resolving mediators in the Scn1a+/- mouse model of Dravet syndrome.

Dravet syndrome is a severe, intractable epilepsy in which 80 % of patients have a de novo mutation in the gene SCN1A. We recently reported that a high seizure burden increased hippocampal concentrations of an array of pro-inflammatory prostaglandins in the Scn1a+/- mouse model of Dravet syndrome. This raised the possibility that a high seizure burden might also trigger the accumulation of specialized pro-resolving mediators that facilitate the resolution of neuroinflammation and brain repair. The present study therefore aimed to examine whether a high seizure burden increased hippocampal concentrations of various specialized pro-resolving mediators in the Scn1a+/- mouse model of Dravet syndrome. Scn1a+/- mice at postnatal day 21 (P21) were primed with a single hyperthermia-induced seizure event to induce a high seizure burden. On P24 primed Scn1a+/- mice with a high seizure burden, unprimed naïve Scn1a+/- mice and wild-type (WT) mice were euthanized and hippocampal tissue was collected for analysis of various specialized pro-resolving mediators using liquid chromatography mass spectrometry. Scn1a+/- mice with a high seizure burden showed increased hippocampal concentrations of the pro-inflammatory leukotrienes B4 and E4. Further, a high seizure burden increased hippocampal concentrations of various special pro-resolving mediators, including the maresins (maresin1), D-series resolvins (RVD1 and RVD4), and protectin (PCTR1). To further characterize these changes, we determined the mRNA expression of lipoxygenase genes, as these synthetic enzymes are common across classes of specialized pro-resolving mediators. However, hippocampal expression of Alox5, Alox12 and Alox15 were not influenced by a high seizure burden. We report for the first time that a high seizure burden increases the hippocampal concentrations of various specialized pro-resolving mediators in Scn1a+/- mice. This provides a platform for future studies to examine whether modulation of these mediators might be exploited to reduce seizures and facilitate brain repair in intractable epilepsy syndromes.

A Comprehensive Unit-Based Safety Program to Enhance the Safe Management of Children in an Epilepsy Monitoring Unit.

BACKGROUND: The epilepsy monitoring unit (EMU) is an independent unit for video electroencephalogram monitoring of epilepsy patients, with the aim of capturing typical seizure events. Because of the uncontrolled seizure frequency, there may be safety risks for patients, such as falling, trauma, aspiration, status epilepticus, and sudden unexpected death in epilepsy. This study aims to evaluate the application effect of comprehensive safety projects on safety management in a pediatric EMU. METHODS: From June 2022 to May 2024, a preevaluation and postevaluation study was conducted at the EMU of a tertiary grade A children's specialized hospital in China. The intervention group carried out a unit-based comprehensive safety project, including nurse safety culture training, establishing a normalized mechanism for safety improvement, and developing evidence-based safety prevention strategies. RESULTS: The response time of paroxysmal events in the intervention group was shorter than that in the control group (P < .05). The scores of implementation of safety precautions and emergency measures in the intervention group were higher than those in the control group (P < .05). The nursing-related complications in the intervention group were lower than those in the control group (P < .05). CONCLUSION: The comprehensive unit-based safety project management mode can effectively improve the quality of EMU nursing care for children and reduce nursing-related complications.

LEVERAGING EPILEPSY DETECTION ACCURACY THROUGH BURST ENERGY INTEGRATION IN CWT AND DECISION TREE CLASSIFICATION OF EEG SIGNALS

The biomedical field plays a pivotal role in advancing healthcare by leveraging technological innovations to enhance diagnostics and treatment strategies. In the context of neurological disorders, particularly epilepsy, automated EEG signal processing stands out as a critical facet of biomedical research. The ability to analyze and interpret vast amounts of electroencephalogram (EEG) data using sophisticated techniques, such as Continuous Wavelet Transform (CWT), contributes significantly to the timely and accurate detection of epileptic events. Automated EEG signal processing, which uses advanced algorithms and pattern recognition methods, enables the identification of subtle yet crucial patterns indicative of epileptic activity. This paper presents an in-depth exploration of epilepsy identification using the CWT on the CHB-MIT Scalp EEG database. The study uses nine complex mother wavelets from the Gaussian and Morlet families to look at 8920 EEG segments, including 197 seizure events. The performance of each wavelet in detecting epileptic convulsions within EEG signals is rigorously evaluated. Our study shows that the complex Gaussian wavelet of order 5 (cgau5) emerged as the optimal choice, with a sensitivity of 97.58% and a precision of 97.93%. To improve epilepsy detection, we introduce burst energy, a novel engineered feature. This method gets accurate information about brain activity from the CWT scalogram by detecting ictal and ictal-free EEGs at different energy levels. The use of burst energy has a significant impact on classification performance, highlighting its potential for improved accuracy in epilepsy identification. This comprehensive study contributes valuable insights into selecting appropriate wavelets and introduces an innovative feature for more effective EEG-based epilepsy detection.

Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation

Objective.This study proposes a closed-loop brain-machine interface (BMI) based on spinal cord stimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach that learns from Local Field Potential (LFP) patterns acquired on the pre-ictal (preceding the seizure) condition.Approach.LFP epochs from the hippocampus and motor cortex are band-pass filtered from 1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, and successively calculate the phase lock values (PLV). As a novelty, theZ-score-based PLV normalization using both modifiedk-means and Davies-Bouldin's measure for clustering is proposed here. Consequently, a generic seizure's detector is calibrated for detecting seizures on the normalized PLV, and enables the spinal cord stimulation for periods of 30 s in a closed-loop, while the BMI system detects seizure events. To calibrate the proposed BMI, a dataset with LFP signals recorded on five Wistar rats during basal state and epileptic crisis was used. The epileptic crisis was induced by injecting pentylenetetrazol (PTZ). Afterwards, two experiments without/with our BMI were carried out, inducing epileptic crisis by PTZ in Wistar rats.Main results.Stronger seizure events of high LFP amplitudes and long time periods were observed in the rat, when the BMI system was not used. In contrast, short-time seizure events of relative low intensity were observed in the rat, using the proposed BMI. The proposed system detected on unseen data the synchronized seizure activity in the hippocampus and motor cortex, provided stimulation appropriately, and consequently decreased seizure symptoms.Significance.Low-frequency LFP signals from the hippocampus and motor cortex, and cord spinal stimulation can be used to develop accurate closed-loop BMIs for early epileptic seizures inhibition, as an alternative treatment.

Epilepsy adverse events post vaccination

Aim: Seizure and epilepsy adverse events (AEs) can occur following vaccination. For epilepsy AEs, they are generally expected to only occur at background population frequencies without associations with immunizations. The Vaccine AEs Reporting System (VAERS) collects a subset of AEs experienced by vaccinees, including multiple epilepsy related AEs. This study examines the possibility of immunization associated epilepsy AEs in VAERS occurring above background rates. Methods: Herein, VAERS is retrospectively examined for epilepsy and seizure AEs following immunizations (AEFIs). Reported AEFIs are normalized by total AEFIs for each vaccine. VAERS data is examined by vaccine type, vaccine source, vaccinee gender, and age-stratified for infants. Results: Association signals for examined epilepsy and seizure AEFIs are identified for multiple vaccines when compared to other vaccines with normalized frequencies above expected population background frequencies. Normalized epilepsy AEFI frequencies for children less than 1 year are higher than children aged 1 year for several vaccines. For pairs of matched vaccines from different manufacturers, statistically different epilepsy AEFI normalized frequencies were observed. These matched pairs for multiple vaccines implicate likely vaccine contaminations (e.g., endotoxins) as likely candidates for causing elevated epilepsy and seizure AEFIs. Conclusions: Based on the reported results, delaying some immunizations of a small set of vaccines until children are 1 year of age is predicted to reduce epilepsy AEFI occurrences for these vaccines. For several vaccines, statistically significant differences in epilepsy AEFI normalized frequencies were detected for the same (or similar) vaccine from different manufacturers; this suggests that possible manufacturing contaminant(s) (e.g., endotoxins) as the likely causative agent(s) for observed epilepsy AEFIs above background rates. Eliminating or reducing these possible contaminants is predicted to reduce the observed associations closer to background population levels observed for other vaccines with very low epilepsy AEFI normalized frequencies.

Overdose as a complex contagion: modelling the community spread of overdose events following law enforcement efforts to disrupt the drug market

BackgroundThe opioid overdose mortality crisis in the USA is an ongoing public health epidemic. Ongoing law enforcement strategies to disrupt local unregulated drug markets can have an iatrogenic effect of...

Analysis of Psychotropic Substance Seizures Occurring in 2017–2022 in Tamaulipas, a Mexican Border State with USA

The trafficking of illegal substances is a global issue. Tamaulipas, a northeastern state in Mexico, is strategically located for drug trafficking to the United States by organized crime. In this study, drug seizure events conducted by the Mexican government in the main cities of Tamaulipas between 2017 and 2022 are analyzed. It was noticed that there was a decrease in events from 2017 to 2020, followed by a slight increase in 2021. Marijuana was the most seized drug, with cocaine and amphetamines following behind. The frequency of drug seizure events was higher in the border cities of Nuevo Laredo, Reynosa, and Matamoros due to their international commercial bridges with the USA. Nuevo Laredo and Reynosa showed a high amount of marijuana seized. In Matamoros, a coastal city, the quantity of cocaine seized was the highest. Results suggest that substances seized were intended to be illegally transported to the USA through Mexican border cities.

Ictal and Postictal Central Apnea in DEPDC5-Related Epilepsy.

DEPDC5-related epilepsy carries an increased risk of sudden unexpected death in epilepsy. We evaluated the occurrence and features of ictal central apnea (ICA) in patients with pathogenic sequence variant in DEPDC5. We reviewed data of 108 patients collected in 2 independent cohorts of patients with focal epilepsy who prospectively underwent long-term video-EEG monitoring (LTVM) with cardiorespiratory polygraphy. All patients underwent (1) at least an overnight polysomnography, (2) a high-field (3T) brain MRI study, and (3) CSF analysis when clinically indicated. Genetic testing (next-generation sequencing [NGS]) was offered for diagnostic purposes to patients with focal epilepsy of unknown etiology. In this cohort, NGS was finally performed in 29 patients, resulting in DEPDC5 pathogenic mutations in 5 patients. According to the presence of ictal apnea events, 5 of 14 patients with ICA showed pathogenic DEPDC5 variants (35%) while none of the 15 patients without ICA showed pathogenic mutation. Notably, DEPDC5 patients showed ICA in all recorded seizures (n = 15) with apnea duration ranging from 20 seconds to more than 1 minute. All seizures were characterized by motor arrest without overt automatic behaviors during ictal apnea. Scalp EEG showed the involvement of temporal lobe leads in all events. Severe oxygen desaturation was observed in 2 cases. In our cohort, ictal central apnea was a common finding in DEPDC5. These results support (1) the need for respiratory polygraphy during LTVM in DEPDC5-related epilepsy and (2) the potential relevance of genetic testing in patients with focal epilepsy of unknown etiology and ictal apnea.

Pro-Ictal EEG Scheduling Improves the Yield of Epilepsy Monitoring: Validating the Use of Multiday Seizure Cycles to Optimize Video-EEG Timing.

A significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. In this study, we introduce and validate "pro-ictal EEG scheduling", a method to schedule vEEG monitoring to coincide with periods of increased seizure likelihood as a low-risk approach to enhance the diagnostic yield. A database of long-term ambulatory vEEG monitoring sessions (n = 5,038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events) to generate cycle-based estimates of seizure risk. In adults, vEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (n = 305) and compared to remaining studies (baseline: n = 3,586). Test of proportions and risk-ratios (RR) were applied to index differences in proportions and likelihood of capturing outcome measures (abnormal report, confirmed seizure, and diary event) during monitoring. The impact of clinical and demographic factors (age, sex, epilepsy-type, and medication) was also explored. During vEEG monitoring, the high-risk group was significantly more likely to have an abnormal vEEG report (190/305:62% vs 1,790/3,586:50% [%change = 12%], RR = 1.25, 95% confidence interval [CI] = [1.137-1.370], p < 0.001), present with a confirmed seizure (56/305:18% vs 424/3,586:11% [%change = 7%], RR = 1.63, 95% CI = [1.265-2.101], p < 0.001) and report an event (153/305:50% vs 1,267/3,586:35% (%change = 15%), RR = 1.420, 95% CI = [1.259:1.602], p < 0.001). Similar effects were observed across clinical and demographic features. This study provides the first large-scale validation of pro-ictal EEG scheduling in improving the yield of vEEG. This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management. ANN NEUROL 2024;96:1148-1159.

Anxiolytic and Anticonvulsant Potential of Biosynthetic Limonene Derivatives in Adult Zebrafish.

This study investigated the anxiolytic and anticonvulsant effects and safety profile of limonene enantiomers and their oxidized derivatives. The toxicity test was performed by monitoring the animals for 96 hours, with no deaths or significant toxicity observed up to the highest dose, which allowed the determination of the LD50. Doses of 4, 20 and 40 mg/kg were tested, with no toxicity observed up to 96 h (LD50>40 mg/kg). Anxiolytic activity was measured in a preference test for light and dark areas, and the effect of the compounds was evaluated in the presence of serotonergic antagonists. The (S)-(-)-LIM and (R)-(+)-LIM enantiomers showed anxiolytic effects, with (S)-(-)-LIM being effective at all doses. In the anticonvulsant test, the oxidized derivatives, such as perilyl acid (PAC), significantly delayed PTZ-induced seizures, an effect blocked by flumazenil (FMZ). The oxidized derivatives, especially perilyl acid (PAC), showed anxiolytic effects at all doses and significantly delayed the three PTZ-induced seizure events. This effect was blocked by FMZ, suggesting a relationship between PAC and the GABAergic pathway. PAC, being the most oxidized derivative, was the most effective for both anxiety and delaying seizure progression, suggesting that oxidation of limonene compounds may increase their therapeutic efficacy.

Seizure prophylaxis in pneumococcal meningitis, cohort study.

Our aim was to assess seizure development as a complication of pneumococcal meningitis and its possible prevention with antiseizure medication prophylaxis. Antiseizure medication (ASM) prophylaxis has been practiced for a long time at our center. We assessed all cases of community-acquired pneumococcal meningitis admitted from January 2010 to April 2021 recorded in our prospective database and conducted further retrospective studies. Of the 86 cases recorded, 21 (24.4%) developed acute symptomatic seizures, more than half of which (11/21; 52.4%) before admission. Seizure development increased the need for orotracheal intubation and intensive care unit admission, while also lengthening hospital stays and suggesting more risk of death and disability at discharge [adjusted odds ratio (aOR), 3.13; 95% confidence interval (CI): 1-9.8]. Of the 74 patients eligible for ASM prophylaxis, 64 received it and 10 did not. ASM prophylaxis seemed effective in preventing seizure development, as only six seizure events were recorded in 64 patients with ASM prophylaxis (9.4%) compared with four in the 10 patients without prophylaxis (40%). Its preventive capacity was especially notable when administered within 4 h of admission. Differences in mortality did not reach statistical significance. Adverse effects were rare. Seizure development is a common complication in pneumococcal meningitis and is associated with increased risks of Intensive Care Unit admission, orotracheal intubation, and longer hospital stays. ASM prophylaxis may be effective in blocking seizure development in patients with preventable seizures and may be associated with better prognosis. Further studies are now warranted. Infection of the meninges (the covering of the brain) due to the common bacteria S pneumoniae, used to be a fatal disease before the introduction of antibiotics and corticoids. Thanks to these drugs, more people survive this disease but, due to the frequent complications, they may have several sequelae. Seizures are a common complication. Our study suggests that they might be prevented by using antiseizure drugs which may reduce both severity and hospital stay.

Association of dietary fiber intake with epileptic seizures in U.S. adults: A Population-base study of 13,277 participants

ObjectiveEpilepsy, a neurological disorder, is identified by the presence of recurrent seizures. We aimed to detect dietary fiber intake and its association with epilepsy prevalence in U.S. adults. MethodsThis cross-sectional study obtained data from the 2013–2018 National Health and Nutrition Examination Survey database. Univariate and multivariate logistic regression models were employed to estimate the association between dietary fiber intake and epilepsy prevalence. The restricted cubic spline (RCS) model was also applied to investigate the dose-response relationships between dietary fiber intake and epileptic seizure events(ESEs). ResultsOur final sample included 13,277 NHANES participants, with the average prevalence of ESEs being 1.09 % (145/13277). After adjusting for all confounding factors, the third quartile of dietary fiber intake levels remained significantly associated with a decreased risk of ESEs[odds ratios (OR) 0.54,95 % confidence interval (CI) 0.33–0.88, P = 0.014)] compared to the first quartile. Higher fiber intake indicated a stable negative association with ESEs in the multivariate logistic regression analysis, weighted generalized additive model. A nonlinear dose-response relationship was observed between dietary fiber intake levels and decreased ESEs risk (P for overall=0.017, P for nonlinear=0.155). Interaction tests showed no significant effect of demographic and disease status on the association between dietary fiber intake and ESEs. ConclusionIn this cross-sectional study, people with a high dietary fiber intake were at a reduced risk of ESEs. However, further prospective studies are needed to investigate the effect of dietary fiber intake in epilepsy events and to determine causality.

First-Generation Antihistamines and Seizures in Young Children

The widespread use of antihistamines in children for treatment of common cold symptoms and their central nervous system effects, like drowsiness, underscore the importance of being aware of the associated risks. To assess associations between prescriptions of first-generation antihistamines and seizures in children using a comprehensive and nationwide dataset. This cohort study used a self-controlled case-crossover design. Data were obtained from the National Health Insurance Service database in Korea. Children born between January 1, 2002, and December 31, 2005, who visited the emergency department for seizure events (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes R56.8, G40, and G41) during the follow-up period were included. Follow-up was completed on December 31, 2019, and data were analyzed from June 3, 2023, to January 30, 2024. First-generation antihistamine prescription. Primary outcome consisted of an index seizure event. Odds ratios (ORs) for seizure events were estimated using a conditional logistic regression model, comparing first-generation antihistamine prescription 1 to 15 days before seizure (hazard period) against control period 1 (31-45 days before the event) and control period 2 (61-75 days before the event) using the same period windows. Stratified analyses were conducted to examine the association with individual participant characteristics. Of 11 729 children who had a seizure event, 3178 (1776 [55.9%] boys) were identified as having been prescribed antihistamines during the hazard or the control period, but not both. Seizure events were predominantly observed in children aged 6 to 24 months (985 [31.0%]) and 25 months to 6 years (1445 [45.5%]). During the hazard period, 1476 first-generation antihistamine prescriptions were recorded, in contrast to 1239 and 1278 prescriptions during control periods 1 and 2, respectively. After multiple confounder adjustments, first-generation antihistamine prescription was associated with an increased seizure event risk during the hazard period (adjusted OR [AOR], 1.22 [95% CI, 1.13-1.31]). Stratified subgroup analyses showed consistent results, particularly in children aged 6 to 24 months who were prescribed first-generation antihistamines having a higher risk (AOR, 1.49 [95% CI, 1.31-1.70]) than children aged 25 months to 6 years (AOR, 1.11 [95% CI, 1.00-1.24]; P = .04 for interaction). Furthermore, sensitivity analyses, including adjustment for exposure window periods, evaluation of new first-generation antihistamine prescriptions, comparison of control points from the same period 1 year prior, and exclusion of individuals using combination drugs, confirmed a similarly high risk. In this cohort study, prescriptions for first-generation antihistamines were associated with a 22.0% higher seizure risk in children, especially in those aged 6 to 24 months. These findings emphasize the need for careful and judicious prescription of first-generation antihistamines in young children and underline the need for further research to elucidate associations between antihistamine prescriptions and seizure risk.