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CNT@NiFeO nanocomposite for phenobarbital removal: adsorption study and characterization.
Environ Sci Pollut Res Int
December 2024
Laboratório de Materiais Magnéticos Nanoestruturados (LaMMaN), Universidade Franciscana (UFN), Santa Maria, RS, Brazil.
The contamination of seas, rivers, lakes, and groundwater by industrial, hospital, and domestic effluents is a global health problem. Scientific approaches are needed to assess and mitigate the impacts of those pollutants, seeking more sustainable alternatives that meet established environmental standards. Among the various contaminants that are released into water sources, phenobarbital (PHEN), a long-acting barbiturate, applied as a hypnotic, sedative, and in the treatment of seizures is an aquatic pollutant, raises significant concerns for human health and the environment.
View Article and Find Full Text PDFUsing high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone.
Commun Med (Lond)
November 2024
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
Background: While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.
Methods: In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data.
Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.
Sci Rep
November 2024
Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland.
Article Synopsis
- The study investigates the impact of larger high-quality datasets versus the latest deep learning techniques on performance gains in modeling neonatal EEG data.
- Results show that while deep learning advances yield better performance when using AUC as a metric, larger datasets are more beneficial when AUC90 or AUC-PR metrics are used.
- The research introduces a new model that outperforms current methods in neonatal seizure detection and provides a way to fine-tune it for specific sites, along with code and model weights for public access.
ECG-based epileptic seizure prediction: Challenges of current data-driven models.
Epilepsia Open
November 2024
Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Objective: Up to a third of patients with epilepsy fail to achieve satisfactory seizure control. A reliable method of predicting seizures would alleviate psychological and physical impact. Dysregulation in heart rate variability (HRV) has been found to precede epileptic seizures and may serve as an extracerebral predictive biomarker.
View Article and Find Full Text PDFPharmacotherapeutic strategies for drug-resistant epilepsy in children.
Epilepsy Behav
December 2024
Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesu' Children's Hospital, IRCCS, Full Member of European Reference Network on Rare and complex Epilepsies EpiCARE, Rome, Italy; University Hospitals KU Leuven, Belgium.
Drug resistance is defined as the failure of adequate trials of two tolerated and appropriately chosen antiseizure medications to achieve sustained seizure freedom. In case of uncontrolled seizures, pseudo-drug-resistance (poor compliance, a worsening effect of an antiseizure medication, a diagnosis of psychogenic non-epileptic seizure) should be first ruled out in case of pediatric epilepsies. This paper discusses the process of choosing antiseizure medication and the concepts of rationale polytherapy and precision medicine.
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