Search Results (256)

Background/Objective: Drug-related problems (DRPs) are a common, potentially avoidable cause of emergency department (ED) use. In December 2022, our hospital integrated a pharmacist-led intervention into routine ED practice. This intervention comprised medication optimization, adherence counseling, and coordinated hand-off to primary care. We quantified 30- and 90-day reconsultations after discharge and explored factors associated with DRP-related revisits. Methods: A retrospective cohort of adults (≥18 years) who attended a tertiary ED (Barcelona, Spain). We included index DRP visits from 1 December 2022 to 30 June 2024. All received the bundle. Demographic, clinical, and pharmacotherapeutic data were extracted from the Catalan Shared Health Record; an independent committee classified revisits as a DRP or non-DRP. Predictors of 30-day DRP revisits were assessed with multivariable logistic regression. Results: Among 1247 patients (mean age 78.6 ± 16.2 years; 59.2% women; and median nine drugs), 120 (9.6%) reconsulted the ED within 30 days, and 194 (15.5%) within 90 days for any cause. DRP-specific rates were 30.8% (37/120) at 30 days and 26.3% (51/194) at 90 days; 81% and 80% of these revisits, respectively, involved a recurrence of the same DRP. The most frequent index DRPs were constipation (14.2%), gastrointestinal bleeding (9.2%), hypertension (8.3%), seizures (8.3%) and hyponatraemia (6.7%). An age ≥ 80 years independently predicted fewer 30-day DRP revisits (OR 0.32; 95% CI 0.13–0.79); hypertension and cognitive impairment were not significant after adjustment. Conclusions: In this single-arm implementation cohort, overall, 30-day ED reconsultations were 9.6% and about one-third were DRP-related, predominantly recurrences, and chiefly gastrointestinal bleeding and seizures. These descriptive findings should be interpreted cautiously given potential survivorship bias and residual confounding; the apparently lower risk among patients aged ≥ 80 years is hypothesis-generating and may reflect geriatric care pathways and caregiver engagement. Targeted post-discharge monitoring for high-recurrence DRPs may help reduce avoidable ED use, and future evaluations should test this in quasi-experimental or randomized designs. Full article

Background: Post-traumatic epilepsy (PTE) is a recognized complication of traumatic brain injury (TBI), yet its risk following mild and moderate TBI remains underappreciated. Although mild TBI represents the majority of cases in clinical practice, a subset of patients develop unprovoked seizures months or even years post-injury. This review aims to synthesize current evidence on the incidence and predictors of PTE in mild and moderate TBI and to propose a clinically actionable decision-support tool for early risk stratification. Methods: We performed a narrative review of peer-reviewed studies published between 1985 and 2024 that reported on the incidence, risk factors and predictive models of PTE in patients with mild (Glasgow Coma Scale [GCS] 13–15) and moderate (GCS 9–12 or imaging-positive) TBI. Data from 24 studies were extracted, focusing on neuroimaging findings, early post-traumatic seizures, EEG abnormalities and clinical risk factors. These variables were integrated into a rule-based algorithm, which was implemented using Streamlit to enable real-time clinical decision-making. The decision-support tool incorporated five domains: injury severity, early post-traumatic seizures, neuroimaging findings (including contusion location and hematoma type), clinical and demographic variables (age, sex, psychiatric comorbidities, prior TBI, neurosurgical intervention) and EEG abnormalities. Results: PTE incidence following mild TBI ranged from <1% to 10%, with increased risk observed in patients presenting with intracranial hemorrhage or early seizures. From moderate TBI, incidence rates were consistently higher (6–12%). Key predictors included early seizures, frontal or temporal contusions, subdural hematoma, multiple contusions and midline shift. Additional risk-enhancing factors included prolonged loss of consciousness, male sex, psychiatric comorbidities and abnormal EEG patterns. Based on these features, we developed a decision-support tool that stratifies patients into low-, moderate- and high-risk categories for developing PTE. Conclusions: Even in non-severe cases, patients with mild and moderate TBI who exhibit high-risk features remain vulnerable to long-term epileptogenesis. Our proposed tool provides a pragmatic, evidence-based framework for early identification and follow-up planning. Prospective validation studies are needed to confirm its predictive accuracy and optimize its clinical utility. Full article

This study evaluated the utility of the 2HELPS2B score in predicting seizures from routine electroencephalographs (rEEGs). In total, 670 rEEGs obtained in a medical intensive care unit (MICU) between October 2018 and March 2023 were analyzed. More than 75% of these rEEGs were requested due to seizures and unexplained altered consciousness. Seizures occurred most frequently in patients with rEEGs characterized by brief, potentially ictal rhythmic discharges and electrographic seizures. A history of seizures was the most prevalent risk factor identified by the 2HELPS2B score. Seizures occurred in 28% of the cohort who experienced a seizure within 24 h of the rEEG and in 38% of the cohort who experienced a seizure before MICU discharge. Among the patients with suspected altered consciousness, the seizure incidence before MICU discharge (9.2%) was twice that within 24 h of the initial rEEG (4.7%). The seizure rate also increased from 12% for a 2HELPS2B score of 1 to 100% for scores ≥ 4. A score ≥ 2 was the optimal cutoff for predicting post-rEEG seizures and guiding antiseizure medication (ASM) treatment. Seizures occurred most frequently in patients whose ASMs were supplemented with new medications, and most new prescriptions for antiseizure medication were issued to patients with altered consciousness. These results demonstrate that the 2HELPS2B score can effectively predict seizures on the basis of rEEG results. Full article

Background: Epilepsy is a chronic, non-communicable brain disorder characterized by recurrent seizures. Some derivatives of 1,2,3,4-tetrahydroisoquinolines have demonstrated anticonvulsant effects. This study aims to investigate the effects of 33 derivatives of 1-aryl-1,2,3,4-tetrahydroisoquinoline on seizures induced by nicotine and strychnine. Methods: The anticonvulsant effects of 1-aryl-1,2,3,4-tetrahydroisoquinoline derivatives were evaluated in white male mice. Convulsant agents were administered subcutaneously at doses of 10.0 mg/kg for nicotine and 1.5 mg/kg for strychnine, 60 min after the oral administration of the test compounds at doses ranging from 0.1 to 10 mg/kg. The onset time, duration of tremors and seizures, and survival rate of the animals were recorded. The docking studies were conducted for 32 tested compounds targeting the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor (PDB ID: 1FTL). Furthermore, a predictive ADMET study was conducted to evaluate the pharmacokinetic and toxicity profiles of the compounds. Results: Compounds 20 and 25 exhibited the highest activity against strychnine-induced seizures. When evaluating the effects of 1-aryl-1,2,3,4-tetrahydroisoquinolines and reference drugs on the tremorogenic and convulsive actions of nicotine at doses of 0.1–5 mg/kg, compounds 3, 6, 8, 14, 16, 25, 27, 29, 30, 31, and 34 demonstrated comparable activity to the reference drugs. The docking results targeting AMPA (PDB ID: 1FTL) revealed comparable binding interactions for most of the compounds, with a (−)C-Docker interaction energy range of 33.82–45.41 Kcal/mol, compared to that of the ligand (41.60 Kcal/mol). The structural requirements of the studied scaffold were analyzed to identify the essential pharmacophoric features for anticonvulsant activity. Furthermore, a predictive ADMET study was conducted to evaluate the pharmacokinetic and toxicity profiles of the compounds. Conclusions: Certain derivatives of 1,2,3,4-tetrahydroisoquinolines may serve as potential anticonvulsant agents for epilepsy. Full article

Background: Epilepsy is a cluster of central nervous system (CNS) disorders identified by recurrent seizures, which affects about 60 million people around the world. In this research, a total of 40 types of 3,4,5-trimethoxycinnamic acid (TMCA) piperazine amide derivatives were designed and synthesized, inspired by the traditional Chinese medicine (TCM) herb pair drugs Polygala tenuifolia and Acori tatarinowii, followed by determination of their anticonvulsant potency. Methods: All the TMCA analogues were tested for their anticonvulsant potential through two acute models of seizures induced in mice: the maximal electroshock (MES) and sc-pentylenetetrazole (PTZ) models. In addition, the lactate dehydrogenase (LDH) inhibitory activity was determined in vitro. Results: The results showed that compounds A3, A9, A12, A14, B9, and B12 exhibited preferable anticonvulsant activity in the primary evaluation. In addition, the molecular docking results predicted good interactions of screened analogues with the LDH. Molecular dynamic simulation was used to reveal the consensual binding affinity between the most promising compound (B9) and active site interactions with LDH. Electroencephalogram (EEG) analysis and silver and immunofluorescence staining were performed to illustrate the anti-epilepsy potential of compound B9. Conclusions: Novel derivatives in this study provide new cores for the further design and optimization inspired by TCM herb pair drugs P. tenuifolia and A. tatarinowii, with the aim to explore new anticonvulsant agents. Full article

Epileptic seizure prediction remains a critical challenge in neuroscience and healthcare, with profound implications for enhancing patient safety and quality of life. In this paper, we introduce a novel seizure prediction method that leverages electroencephalogram (EEG) data, combining discrete wavelet transform (DWT)-based time–frequency analysis, advanced feature extraction, and deep learning using Fourier neural networks (FNNs). The proposed approach extracts essential features from EEG signals—including entropy, power, frequency, and amplitude—to effectively capture the brain’s complex and nonstationary dynamics. We measure the method based on the broadly used CHB-MIT EEG dataset, ensuring direct comparability with prior research. Experimental results demonstrate that our DWT-FS-FNN model achieves a prediction accuracy of 98.96 with a zero false positive rate, outperforming several state-of-the-art methods. These findings underscore the potential of integrating advanced signal processing and deep learning methods for reliable, real-time seizure prediction. Future work will focus on optimizing the model for real-world clinical deployment and expanding it to incorporate multimodal physiological data, further enhancing its applicability in clinical practice. Full article

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

The radiological finding of Dark White Matter (DWM)—characteristic diffuse subcortical white matter hypointensity on T2/FLAIR sequences—is underrecognized, but has important clinical implications. Recent systematic evidence shows that over 60% of previously published cases showed seizures in association with DWM findings—it is also particularly predictive of the underlying etiology, particularly non-ketotic hyperglycemic hyperosmolar state (NKH). Based on our previous work, we reinterpret the data, focusing only on patients with seizures and DWM, to summarize the most essential and distinguishing features of these patients. Both cortical and subcortical abnormalities in DWM are more frequently associated with anti-MOG encephalitis. DWM with or without cortical involvement is more commonly found in NKH among patients with seizures. This updated systematic review will describe the proposed pathophysiological mechanisms, clinical associations, and implications for DWM in patients with seizures, and highlight how early recognition of DWM may allow for targeted diagnostic strategies and treatment options. We expanded our previous search with details regarding seizure features, our results show that DWM is associated with repetitive seizures and Status Epilepticus (both convulsive and non), in line with other peri-ictal MRI abnormalities associated with prolonged seizure activity. DWM-associated seizures are mostly focal, rather than generalized. Moreover, the high percentage of clinical recovery at follow-up suggests that DWM may be predictive of a good outcome, especially in NKH cases, although this needs to be confirmed in future studies. 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

Epileptic seizures result from abnormal brain activity, posing significant health risks due to their sudden and unpredictable nature. Accurate seizure prediction is crucial for improving patient outcomes and enabling timely interventions. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL), have significantly enhanced seizure detection and prediction. This review provides a comprehensive overview of seizure prediction models that integrate temporal and spectral features as inputs or enhanced representations for ML and DL models. Emphasizing convolutional neural networks (CNNs) and other deep architectures, we explore the role of time-domain and frequency-domain features, such as wavelet transforms, short-time Fourier transforms, and spectrogram representations, in improving model performance. Additionally, the review discusses common challenges, including feature interpretability, generalizability across datasets, and computational efficiency. By highlighting recent advancements and limitations, this study provides insights into optimizing spectral and temporal feature integration for seizure prediction, paving the way for more robust and clinically viable AI-based solutions. Full article