Chronic Brain Disorder Research Articles

A Comprehensive Analysis on Galantamine Based Hybrids for the Management of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive chronic age-related neurodegenerative brain disorder characterized by the loss of memory and other cognitive functions. The exact etiology of AD is still under investigation, however several factors such as low level of neurotransmitter acetylcholine (ACh), aggregation of amyloid beta (Aβ) in the form of Aβ plaques, hyperphosphorylation of tau protein into neurofibrillary tangles (NFTs), oxidative stress, and metal ion imbalance are the major hallmarks of this disease. Of the multiple hypotheses for AD, the amyloid-β (Aβ) and cholinergic hypothesis are the main targeting hypotheses for AD. Some researchers hypothesized that the primary event associated with the cholinergic neurotransmitter (acetylcholine) is memory loss and cognitive impairment. Due to the disease's complicated pathogenesis, long-term therapy with a single target candidate is futile. As a result, multitargeted and multifunctional therapies have emerged. Various research teams are concentrating on addressing multiple disease factors through hybridization techniques. Consequently, this hybridization approach has been applied to all core scaffolds, including galantamine. In this article, we tried to provide a thorough overview of the most recent developments on galantamine, a prospective AChE inhibitor, and its hybrid analogs as possible therapeutic agents for treating AD. Furthermore, we also provided the design, synthesis, and SAR analysis of the galantamine-based compounds used in the last decades for the management of AD.

Neurovascular unit, neuroinflammation and neurodegeneration markers in brain disorders.

Neurovascular unit (NVU) inflammation via activation of glial cells and neuronal damage plays a critical role in neurodegenerative diseases. Though the exact mechanism of disease pathogenesis is not understood, certain biomarkers provide valuable insight into the disease pathogenesis, severity, progression and therapeutic efficacy. These markers can be used to assess pathophysiological status of brain cells including neurons, astrocytes, microglia, oligodendrocytes, specialized microvascular endothelial cells, pericytes, NVU, and blood-brain barrier (BBB) disruption. Damage or derangements in tight junction (TJ), adherens junction (AdJ), and gap junction (GJ) components of the BBB lead to increased permeability and neuroinflammation in various brain disorders including neurodegenerative disorders. Thus, neuroinflammatory markers can be evaluated in blood, cerebrospinal fluid (CSF), or brain tissues to determine neurological disease severity, progression, and therapeutic responsiveness. Chronic inflammation is common in age-related neurodegenerative disorders including Alzheimer's disease (AD), Parkinson's disease (PD), and dementia. Neurotrauma/traumatic brain injury (TBI) also leads to acute and chronic neuroinflammatory responses. The expression of some markers may also be altered many years or even decades before the onset of neurodegenerative disorders. In this review, we discuss markers of neuroinflammation, and neurodegeneration associated with acute and chronic brain disorders, especially those associated with neurovascular pathologies. These biomarkers can be evaluated in CSF, or brain tissues. Neurofilament light (NfL), ubiquitin C-terminal hydrolase-L1 (UCHL1), glial fibrillary acidic protein (GFAP), Ionized calcium-binding adaptor molecule 1 (Iba-1), transmembrane protein 119 (TMEM119), aquaporin, endothelin-1, and platelet-derived growth factor receptor beta (PDGFRβ) are some important neuroinflammatory markers. Recent BBB-on-a-chip modeling offers promising potential for providing an in-depth understanding of brain disorders and neurotherapeutics. Integration of these markers in clinical practice could potentially enhance early diagnosis, monitor disease progression, and improve therapeutic outcomes.

Minocycline reduces neurobehavioral deficits evoked by chronic unpredictable stress in adult zebrafish

Chronic stress-related brain disorders are widespread and debilitating, and often cause lasting neurobehavioral deficits. Minocycline, a common antibiotic and an established inhibitor of microglia, emerges as potential treatment of these disorders. The zebrafish (Danio rerio) is an important emerging model organism in translational neuroscience and stress research. Here, we evaluated the potential of minocycline to correct microglia-mediated behavioral, genomic and neuroimmune responses induced by chronic unpredictable stress (CUS) in adult zebrafish. We demonstrated that CUS evoked overt behavioral deficits in the novel tank, light–dark box and shoaling tests, paralleled by elevated stress hormones (CRH, ACTH and cortisol), and upregulated brain expression of the ‘neurotoxic M1′ microglia-specific biomarker gene (MHC-2) and pro-inflammatory cytokine genes (IL-1β, IL-6 and IFN-γ). CUS also elevated peripheral (whole-body) pro-inflammatory (IL-1β, IFN-γ) and lowered anti-inflammatory cytokines (IL-4 and IL-10), as well as reduced whole-brain serotonin, dopamine and norepinephrine levels, and increased brain dopamine and serotonin turnover. In contrast, minocycline attenuated most of these effects, also reducing CUS-elevated peripheral levels of IL-6 and IFN-γ. Collectively, this implicates microglia in zebrafish responses to chronic stress, and suggests glial pathways as potential evolutionarily conserved drug targets for treating stress-evoked neuropathogenesis. Our findings also support the growing translational value of zebrafish models for understanding complex molecular mechanisms of brain pathogenesis and its therapy.

GABA system in the prefrontal cortex involved in psychostimulant addiction.

Drug addiction is a chronic and relapse brain disorder. Psychostimulants such as cocaine and amphetamine are highly addictive drugs. Abuse drugs target various brain areas in the nervous system. Recent studies have shown that the prefrontal cortex (PFC) plays a key role in regulating addictive behaviors. The PFC is made up of excitatory glutamatergic cells and gamma-aminobutyric acid (GABAergic) interneurons. Recently, studies showed that GABA level was related with psychostimulant addiction. In this review, we will introduce the role and mechanism of GABA and γ-aminobutyric acid receptors (GABARs) of the PFC in regulating drug addiction, especially in psychostimulant addiction.

Dramatic Improvements in Patient Understanding of Epilepsy in Iraq

Background: Epilepsy, a chronic brain disorder causing recurrent seizures, affects over 50 million people globally, leading to significant social challenges due to prevalent misconceptions. Objective: This study aims to evaluate patients' knowledge about epilepsy at Baquba Teaching Hospital's neurological department. Methodology: A pre-experimental design with pretest and posttest assessments was conducted from July 11 to December 5, 2023, involving 100 epilepsy patients. Data were gathered using the Epilepsy Knowledge Scale (EKS), with both inferential and descriptive statistical analyses. Results: Patients initially had insufficient knowledge about epilepsy, but the instructional program significantly improved their understanding, as shown by notable pretest and posttest score differences. Novelty: This study highlights the efficacy of instructional programs in enhancing epilepsy knowledge among patients, emphasizing the importance of educational interventions in managing chronic neurological conditions. Implications: The results suggest that instructional programs should be routinely implemented in clinical settings to increase epilepsy awareness and knowledge among patients. Conclusion: The instructional program effectively enhances epilepsy knowledge among patients, suggesting it should be routinely implemented to improve patient education and outcomes, regardless of socio-demographic factors. Highlight: Patients initially had low epilepsy knowledge. Instructional program greatly improved understanding. No link between knowledge and demographics. Keywords: Epilepsy, patient knowledge, instructional program, neurological disorders, Baquba Teaching Hospital

A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals.

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.

Regulatory T cells as a possible new target in epilepsy?

Epilepsy is a complex chronic brain disorder with diverse clinical features that can be caused by various triggering events, such as infections, head trauma, or stroke. During epileptogenesis, various abnormalities are observed, such as altered cellular homeostasis, imbalance of neurotransmitters, tissue changes, and the release of inflammatory mediators, which in combination lead to spontaneous recurrent seizures. Regulatory T cells (Tregs), a subtype of CD4+Foxp3+ T cells, best known for their key function in immune suppression, also seem to play a role in attenuating neurodegeneration and suppressing pathological inflammation in several brain disease states. Considering that epilepsy is also highly associated with neuronal damage and neuroinflammation, modulation of Tregs may be an interesting way to modify the disease course of epilepsy and needs further investigation. In this review, we will describe the currently available information on Tregs in epilepsy.

An EEG Study on [Formula: see text] Phase-Amplitude Coupling-Based Functional Brain Network in Epilepsy Patients.

Epilepsy, a chronic neuropsychiatric brain disorder characterized with recurrent seizures, is closely associated with abnormal neural communications within the brain. Despite that the phase-amplitude coupling (PAC) has been suggested to offer a new way to observe neural interactions during epilepsy, however, few studies pay attention to alterations of the epileptic functional brain network based on PAC, especially on the [Formula: see text] PAC. Therefore, we use scalp electroencephalography (EEG) data of epileptic patients and the [Formula: see text] PAC modulation index (MI) to construct functional brain networks to examine variations of neural interactions during different epileptic phases. Statistically, the findings show that between-channel MI values in the post-ictal period significantly increase compared to that in the pre-ictal period, and the between-channel MI value has a close association with the information of phase and amplitude provided by the channels. Importantly, in both the phase-amplitude and amplitude-phase functional brain networks, the average node degree is remarkably higher in the post-ictal period than that in the pre-ictal period, whereas the characteristic path length in the ictal and post-ictal periods is significantly lower than that in the pre-ictal period. Besides, the average betweenness centrality in the post-ictal period is remarkably higher than that in the ictal period. Interestingly, the positive correlations between within-channel MI values and between-channel MI values can be observed during the pre-ictal, ictal and post-ictal periods. These findings suggest that the [Formula: see text] PAC-based functional brain network may provide a novel perspective to understanding alterations of neural interactions during the epileptic evolution, and may contribute to effectively controlling the spread of epileptic seizures.

Impact of Phytochemicals on Managing Chronic Brain Disorders: Exploring Therapeutic Potential

Impact of Phytochemicals on Managing Chronic Brain Disorders: Exploring Therapeutic Potential

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient’s medical history

BackgroundEpilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy.ResultsIn this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients.ConclusionOur CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

Case Report: EEG Picture in a Patient with Post-Stroke Epilepsy

Introduction: Epilepsy is a chronic brain disorder that affects people all over the world. Seizures are the most common neurologic symptom in stroke patients. Approximately 10% of all stroke patients have experienced seizures, and post-stroke seizures generally begin several years after the stroke. Case Illustration: A male patient, 55 years old, Balinese, Kinan, came to the emergency room of Prof. Dr. I.G.N.G. Ngoerah General Hospital with complaints of seizures. The first seizure occurred on December 19, 2021, with a pattern of glancing at the left eye then, followed by full-body twitching, which lasted approximately 5 minutes and stopped on its own. The patient said that before the seizure, the patient was well conscious; during the seizure, the patient was unconscious, and after the seizure, the patient regained consciousness as before the seizure. Then, the seizure repeated the next day (December 20, 2021) with the same pattern and duration, but the patient was not taken for treatment. On physical examination, the patient was alert with vital signs and general physical examination within normal limits. Neurologic examination revealed no focal neurologic deficits. Electroencephalography (EEG) examination showed intermittent slow activity in the left and right fronto-centro-occipital areas. Computed tomography scan (CT-Scan) examination of the head without contrast revealed a hypodense image in the right temporo-parietal lobe. The patient then received anti-epileptic drug (OAE) therapy, OAE therapy given Phenytoin 200 mg every 12 hours intra-orally, Folic Acid 1 mg every 24 hours intra-orally, and anti-platelet and anti-hypertension continued. Discussion: Post-stroke seizure (PSS) is an episode of convulsion that occurs either single or multiple after a stroke, which is thought to be due to irreversible or reversible brain damage. At the same time, post-stroke epilepsy is convulsions after a stroke that occur at least two revivals without provocation with a distance between revivals of more than 24 hours. PSS classification is divided into early post-stroke seizure, where seizures occur onset two weeks early post-stroke, and late (delayed) post-stroke seizure if seizures occur onset after two weeks post-stroke. Post-stroke seizures' most common electroencephalography (EEG) waves are generalized slow waves, focal slowing, focal sharp and slow waves, and periodic lateralized epileptiform discharges (PLEDs). However, 5.1% of patients have normal EEG. The administration of anti-convulsants to prevent recurrent seizures is recommended by the European Guidelines of The European Stroke Organization. In comparison, prophylaxis is not recommended for patients with post-stroke. Keywords: Post-stroke Epilepsy, Stroke, Electroencephalography

Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques

Parkinson’s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%.

Neuroprotective effects of platinum nanoparticle-based microreactors in bicuculline-induced seizures

Epilepsy designates a group of chronic brain disorders, characterized by the recurrence of hypersynchronous, repetitive activity, of neuronal clusters. Epileptic seizures are the hallmark of epilepsy. The primary goal of epilepsy treatment is to eliminate seizures with minimal side effects. Nevertheless, approximately 30% of patients do not respond to the available drugs. An imbalance between excitatory/inhibitory neurotransmission, that leads to excitotoxicity, seizures, and cell death, has been proposed as an important mechanism regarding epileptogenesis. Recently, it has been shown that microreactors composed of platinum nanoparticles (Pt-NP) and glutamate dehydrogenase possess in vitro and in vivo activity against excitotoxicity. This study investigates the in vivo effects of these microreactors in an animal model of epilepsy induced by the administration of the GABAergic antagonist bicuculline. Male Wistar rats were administered intracerebroventricularly (i.c.v.) with the microreactors or saline and, five days later, injected with bicuculline or saline. Seizure severity was evaluated in an open field. Thirty min after behavioral measurements, animals were euthanized, and their brains processed for neurodegeneration evaluation and for neurogenesis. Treatment with the microreactors significantly increased the time taken for the onset of seizures and for the first tonic-clonic seizure, when compared to the bicuculline group that did not receive the microreactor. The administration of the microreactors also increased the time spent in total exploration and grooming. Treatment with the microreactors decreased bicuculline-induced neurodegeneration and increased neurogenesis in the dorsal and ventral hippocampus. These observations suggest that treatment with Pt-NP-based microreactors attenuates the behavioral and neurobiological consequences of epileptiform seizure activity.

Diagnostic significance and potential function of miR-320d in schizophrenia.

Schizophrenia is a chronic brain disorder and needs objective diagnostic biomarkers. MicroRNAs are highly expressed in the nervous system. The study investigated the expression and clinical values of serum miR-320d in schizophrenia patients. In addition, the underlying mechanism was preliminarily examined via bioinformatic analysis. Serum samples were collected from 57 patients with first-episode schizophrenia and 62 healthy controls. The cognitive function of patients was assessed via Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB) consisting of seven domains. Serum miR-320d levels were tested via qRT-PCR. The miRNA target predictions were obtained from Target Scan, and annotated through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Based on the GSE167630 dataset, downregulated serum miR-320d in schizophrenia was identified, which was determined in the serum of schizophrenia patients. Serum miR-320d presented a conspicuous relationship with MCCB score in both the control group and the schizophrenia group. After adjusting for age, sex, BMI, and education, serum miR-320d was still independently related to the occurrence of schizophrenia. It can identify schizophrenia cases from healthy ones with an AUC of 0.931. The Go enrichment analysis indicated that the target genes were mainly enriched in homophilic cell adhesion and cell-cell adhesion via plasma-membrane adhesion molecules, and GTPase activity and guanosine diphosphate (GDP) binding. Rap1 signaling pathway was enriched via KEGG analysis. Serum miR-320d can be taken as a candidate marker for the diagnosis of schizophrenia. Its regulatory role in neuronal cell adhesion and Rap1 signaling pathway might be the potential underlying mechanism of miR-320d in schizophrenia.

Phillygenin Suppresses Glutamate Exocytosis in Rat Cerebrocortical Nerve Terminals (Synaptosomes) through the Inhibition of Cav2.2 Calcium Channels.

Glutamate is a major excitatory neurotransmitter that mediates neuronal damage in acute and chronic brain disorders. The effect and mechanism of phillygenin, a natural compound with neuroprotective potential, on glutamate release in isolated nerve terminals (synaptosomes) prepared from the rat cerebral cortex were examined. In this study, 4-aminopyridine (4-AP), a potassium channel blocker, was utilized to induce the release of glutamate, which was subsequently quantified via a fluorometric assay. Our findings revealed that phillygenin reduced 4-AP-induced glutamate release, and this inhibitory effect was reversed by removing extracellular Ca2+ or inhibiting vesicular transport with bafilomycin A1. However, exposure to the glutamate transporter inhibitor dl-threo-beta-benzyl-oxyaspartate (dl-TOBA) did not influence the inhibitory effect. Moreover, phillygenin did not change the synaptosomal membrane potential but lowered the 4-AP-triggered increase in intrasynaptosomal Ca2+ concentration ([Ca2+]i). Antagonizing Cav2.2 (N-type) calcium channels blocked the inhibition of glutamate release by phillygenin, whereas pretreatment with the mitochondrial Na+/Ca2+ exchanger inhibitor, CGP37157 or the ryanodine receptor inhibitor, dantrolene, both of which block intracellular Ca2+ release, had no effect. The effect of phillygenin on glutamate release triggered by 4-AP was completely abolished when MAPK/ERK inhibitors were applied. Furthermore, phillygenin attenuated the phosphorylation of ERK1/2 and its major presynaptic target, synapsin I, a protein associated with synaptic vesicles. These data collectively suggest that phillygenin mediates the inhibition of evoked glutamate release from synaptosomes primarily by reducing the influx of Ca2+ through Cav2.2 calcium channels, thereby subsequently suppressing the MAPK/ERK/synapsin I signaling cascade.

Application prospect of CRISPR-Cas9 gene editing in epilepsy

Epilepsy, a chronic noncommunicable brain disorder, is characterized by abnormal electrical activity in the brain, leading to seizures and disruptions in normal brain functions. Despite various known causes, a significant proportion of epilepsy cases remain unexplained. In recent years, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR-Cas9) technology has emerged as a powerful tool for genetic engineering. Compared with the first two generations of gene-editing technology, it has the advantages of low cost, easy design and easy operation. Utilizing CRISPR activation (CRISPRa), researchers have explored the potential of increasing the expression of genes involved in regulating synaptic interactions to control epileptic activity. Studies on transgenic mice have shown that upregulating the Kv1.1 gene (Kcnal1), which encodes for a voltage-gated potassium channel responsible for regulating neuronal excitability, can reduce seizures and improve cognitive function. Additionally, CRISPR-Cas9 has been instrumental in creating animal models to study epilepsy, providing insights into gene functions, disease mechanisms, and potential therapeutic interventions. However, further research is needed to fully explore the potential of CRISPR-based therapies for targeted treatment of epilepsy. This review systematically introduces the pathogenesis of epilepsy, including the origins and causes of epilepsy and the mechanism of seizure formation and further discusses the application of the CRISPR/Cas9 system in epilepsy.

MiRNAs and Substances Abuse: Clinical and Forensic Pathological Implications: A Systematic Review

Substance addiction is a chronic and relapsing brain disorder characterized by compulsive seeking and continued substance use, despite adverse consequences. The high prevalence and social burden of addiction are indisputable; however, the available intervention is insufficient. The modulation of gene expression and aberrant adaptation of neural networks are attributed to the changes in brain functions under repeated exposure to addictive substances. Considerable studies have demonstrated that miRNAs are strong modulators of post-transcriptional gene expression in substance addiction. The emerging role of microRNA (miRNA) provides new insights into many biological and pathological processes in the central nervous system: their variable expression in different regions of the brain and tissues may play a key role in regulating the pathophysiological events of addiction. This work provides an overview of the current literature on miRNAs involved in addiction, evaluating their impaired expression and regulatory role in neuroadaptation and synaptic plasticity. Clinical implications of such modulatory capacities will be estimated. Specifically, it will evaluate the potential diagnostic role of miRNAs in the various stages of drug and substance addiction. Future perspectives about miRNAs as potential novel therapeutic targets for substance addiction and abuse will also be provided.

MCC950 alleviates seizure severity and angiogenesis by inhibiting NLRP3/ IL-1β signaling pathway-mediated pyroptosis in mouse model of epilepsy

Epilepsy is one of the most common serious chronic brain disorders, affecting up to 70 million people worldwide. Vascular disruption, including blood–brain barrier impairment and pathological angiogenesis, exacerbates its occurrence. However, its underlying mechanisms remain elusive. MCC950 is a specific small-molecule inhibitor that selectively blocks NLRP3 inflammatory vesicle activation across the blood–brain barrier, limits downstream IL-1β maturation and release, and exerts therapeutic effects across multiple diseases. In the present study, an epilepsy model was established by intraperitoneal administration of Kainic acid to adult male C57BL/6J wild-type mice. The results revealed that the epilepsy susceptibility of MCC950-treated mice was decreased, and neural damage following seizure episodes was reduced. In addition, immunofluorescence staining, RT-qPCR, and Western blot demonstrated that MCC950 inhibited the expression of the NLRP3 inflammasome and its related proteins in microglia, whereas microangiogenesis was found to be increased in the cerebral cortex and hippocampus of epileptic mice, and these effects could be reversed by MCC950. Furthermore, neurobehavioral impairment was observed in the epileptic mouse model, and MCC950 similarly alleviated the aforementioned pathological process. To the best of our knowledge, this is the first study to establish that pathological microangiogenesis is associated with NLRP3/IL-1β signaling pathway activation in a Kainic acid-induced epilepsy mouse model and that MCC950 administration attenuates the above-mentioned pathological changes and exerts neuroprotective effects. Therefore, MCC950 is a promising therapeutic agent for the treatment of epilepsy.

Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models

“Epilepsy is a chronic brain disorder that affects people of all ages. The cause of epilepsy is often unknown and its effect in different age groups is not yet investigated. The main objective of this study is to introduce a novel approach that successfully detects epilepsy even from noisy EEG signals. In addition, this study also investigates population specific epilepsy detection for providing novel insights. Correspondingly, we utilized the TUH EEG corpus database, publicly available challenging multi-channel EEG database containing detailed patient information. We applied a band-pass filter and manual noise rejection to remove noise and artifacts from EEG signals. We then utilized statistical features and correlation to select channels, and applied different transform analysis methods such as continuous wavelet transform, spectrogram, and Wigner-Ville distribution, with and without ensemble averaging, to construct an image dataset. Afterwards, we used various deep-learning models for general analysis. Our findings suggest that different models such as DenseNet201, DenseNet169, DenseNet121, VGG16, VGG19, Xception, InceptionV3, and MobileNetV2 performed better while using images generated from different approaches in general analysis. Furthermore, we split the dataset into two sections according to age for population analysis. All the models that performed well in the general analysis were used for population analysis, which provided novel insights in epilepsy detection from EEG. Our proposed framework for epilepsy detection achieved 100% accuracy, which outperforms other concurrent methods."

Using Hypnotic Techniques and Guided Imagery in Treatment of a Highly Hypnosible Woman with Slowly Progressing Chronic Organic Brain Disorder: A Case Study

A therapeutic intervention combines guided imagery and hypnotic techniques (including self-hypnosis), direct and indirect suggestions, medical treatments, and highly improving patient condition. A variety of hypnotic intervention techniques were introduced to treat a highly hypnotizable patient suffering from an advanced organic brain disorder due to an autoimmune disease and resistant to traditional medicine, showing no improvement despite being highly motivated and cooperating with conventional measures. Progress in the Patient's condition and symptoms has been achieved during these hypnotic interventions: increased appetite and weight gain with a decrease in symptoms of gastroparesis, decreased limb spasticity while walking, improved speech and pronunciation, improved muscular power, improved balance and posture, pain diminution and improved mood.