Neurological Disorders Research Articles

Potential Risks Associated With Long-term Use of Proton Pump Inhibitors and the Maintenance Treatment Modality for Patients With Mild Gastroesophageal Reflux Disease.

Gastroesophageal reflux disease (GERD) significantly affects the health-related quality of life and healthcare costs. The prevalence of this disease is increasing in Asia, leading to a rapid increase in the demand of proton pump inhibitors (PPIs). Despite effective symptom management during initial treatment, relapse rates after PPI cessation remain high in patients with GERD, warranting longterm maintenance therapy. Concerns regarding potential side effects related to the long-term use of PPIs are escalating with increased usage. Studies have reported diverse side effects of PPIs, such as increased fracture risk, cardiovascular concerns, enteric infections, neurological diseases, and potential associations with gastric cancer. However, definitive causal relationships remain unclear. This review comprehensively summarizes the latest knowledge on the potential risks associated with long-term use of PPIs. Continuous or noncontinuous therapy can be used as a maintenance treatment modality for GERD. For patients with mild GERD, including those with nonerosive and mildly erosive reflux disease, on-demand therapy following a sufficient period of continuous maintenance therapy is recommended as a long-term maintenance treatment option.

Mapping of β3-Adrenergic Receptor in Living Cells with a Ligand-Guided Fluorescent Probe.

Tracking the dynamic distribution of native proteins in living cells or tissues in real time is essential for understanding the functional mechanisms involved in their physiological or pathological processes. The β3-adrenergic receptor (β3-AR) has important biological functions. It is expected to be a diagnostic indicator for aging, yet current probes for β-ARs are unable to dynamically monitor β3-AR in real time, which impedes the progress of β3-AR research. Here, we developed a ligand-directed anchoring probe, β3-ARP, that precisely covalently anchors with native β3-AR in living cells, for the diagnosis of senescence. The ligand-directed anchoring probe selectively recognizes and traces β3-AR and can achieve dynamic observation and monitoring of β3-AR in living cells, including the interaction between lipid droplets and mitochondria. Moreover, we were able to directly probe the distribution of β3-AR in various organs of aging mice in situ and track the location of native β3-AR in different types of primary neuronal cells using β3-ARP and two-photon imaging, which revealed the aberrant accumulation of lipid droplets and the distribution of β3-AR in neurological diseases. This research has resulted in a new covalently anchoring fluorescent probe, β3-ARP, which could serve as a powerful tool to explore the link between β3-AR and age-related diseases.

Conjugated polymers as photocatalysts for hydrogen therapy

AbstractHydrogen as a therapeutic agent has attracted a great deal of attention because of its superior therapeutic outcome on many diseases, including inflammatory injury, tumors, metabolic disorders, and neurological diseases. Photocatalytic hydrogen evolution has emerged as a promising strategy for hydrogen production and delivery. This review article presents the recent developments in the design and synthesis of conjugated polymer materials, including linear polymers and crosslinked conjugated materials, for photocatalytic hydrogen evolution. Particularly, we focus this review on the development of conjugated polymers as photocatalysts and the resulting hydrogen therapy in the fields of anti‐inflammatory, free radical scavenging, and cancer treatment. Finally, this article discusses the future research and perspective of conjugated polymer materials for hydrogen evolution and the potential clinical applications of hydrogen as a therapeutic agent.

In Vivo Insights: Near-Infrared Photon Sampling of Reflectance Spectra from Cranial and Extracranial Sites in Healthy Individuals and Patients with Essential Tremor

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients.

Machine learning techniques for diagnosis of rare diseases from medical images

Introduction/Background: Rare diseases are life-threatening or chronically debilitating conditions that affect a small percentage of the population. Early and accurate diagnosis of rare diseases is challenging due to their complexity and limited understanding. Conventional diagnostic methods are often inadequate, time-consuming, and expensive. Machine learning (ML) has emerged as a promising technique for the analysis of medical images to support the diagnosis of various diseases including rare disorders. ML algorithms can learn complex patterns from large medical imaging datasets to aid clinicians in disease diagnosis, prognosis, and detection of complications. Materials and Methods: A structured search was conducted in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed articles published between 2013 to 2023 related to ML-based diagnosis of rare diseases from medical images. A total of 187 articles were identified after removing duplicates. The titles and abstracts of retrieved articles were screened to determine their relevance based on the research objective. Finally, 51 full-text articles were selected for the final review. The key data extracted from selected studies included diseases, imaging modalities, ML algorithms, performance metrics, datasets, and limitations. Results: Most studies evaluated deep learning-based ML techniques, with convolutional neural networks (CNN) being the most applied algorithm. CNNs were mainly used to classify rare diseases based on specific imaging features in X-rays, CT, and MRI scans. Diseases frequently investigated included cardiovascular, neurological, and rare genetic disorders. Publicly available datasets such as Deep Lesion, MSD, and ChestX-ray14 were commonly utilized. Most studies reported high classification accuracy ranging from 80-95% on test datasets. However, limited generalizability due to small private datasets and lack of external validation were limitations. Discussion: The results demonstrate the potential of deep learning for the image-based diagnosis of rare diseases. Features learned from large imaging data sets enabled CNNs to distinguish subtle abnormalities. Hybrid models combining CNNs with other techniques like recurrent neural networks further improved performance. Overall, ML showed promise as a decision support tool. However, more multi-center validation studies are needed using larger and diverse datasets before clinical adoption. Standardization of performance metrics and reporting is also required to establish reliability and generalizability. Conclusion: This comprehensive review provided insights into ongoing research applying ML to medical images for rare disease diagnosis. While initial results are encouraging, further work is still necessary before ML can be reliably used in clinical decision making. Larger collaborative efforts and open-source datasets are needed to advance the field. Standardization of ML frameworks can also promote reproducible research and comparison of different methodologies.

Being associated with multiple diseases, CD79a, as a B-cell marker plays an important role in disease treatment and prognosis

CD79a, a membrane glycoprotein critical for B-cell receptor (BCR) signaling, plays a vital role in B-cell development and immune responses. It serves as a marker for normal and tumor B-cells and is implicated in the progression of various diseases, including B-cell lymphomas, leukemia, autoimmune disorders, and other systemic diseases such as neurological, hematological, and respiratory disorders. This review provides a comprehensive analysis of the discovery, molecular structure, and function of CD79a, along with its regulatory mechanisms at the transcriptional and post-translational levels. CD79a’s involvement in signaling pathways and its potential as a therapeutic target for novel treatments, such as chimeric antigen receptor-T (CAR-T) cell therapy and bispecific antibodies, are also explored. The review highlights emerging therapeutic strategies targeting CD79a, emphasizing its significance in the prognosis of CD79a-related diseases and the ongoing need for further research to optimize clinical interventions.

Olfactory and trigeminal routes of HSV-1 CNS infection with regional microglial heterogeneity.

Herpes simplex virus type 1 (HSV-1) primarily targets the oral and nasal epithelia before establishing latency in the trigeminal ganglion (TG) and other peripheral ganglia. HSV-1 can also infect and become latent in the central nervous system (CNS) independent of latency in the TGs. Recent studies suggest entry to the CNS via two distinct routes: the TG-brainstem connection and olfactory nerve; however, to date, there is no characterization of brain regions targeted during HSV-1 primary infection. Furthermore, the immune response by microglia may also contribute to the heterogeneity between different brain regions. However, the response to HSV-1 by microglia has not been characterized in a region-specific manner. This study investigated the time course of HSV-1 spread within the olfactory epithelium (OE) and CNS following intranasal inoculation and the corresponding macrophage/microglial response in a C57BL/6 mouse model. We found an apical to basal spread of HSV-1 within the OE and underlying tissue accompanied by an inflammatory response of macrophages. OE infection was followed by infection of a small subset of brain regions targeted by the TG in the brainstem and other cranial nerve nuclei, including the vagus and hypoglossal nerve. Furthermore, other brain regions were positive for HSV-1 antigens, such as the locus coeruleus (LC), raphe nucleus (RaN), and hypothalamus while sparing the hippocampus and cortex. Within each brain region, microglia activation also varied widely. These findings provide critical insights into the region-specific dissemination of HSV-1 within the CNS, elucidating potential mechanisms linking viral infection to neurological and neurodegenerative diseases.IMPORTANCEThis study shows how herpes simplex virus type 1 (HSV-1) spreads within the brain after infecting the nasal passages. Our data reveal the distinct pattern of HSV-1 through the brain during a non-encephalitic infection. Furthermore, microglial activation was also temporally and spatially specific, with some regions of the brain having sustained microglial activation even in the absence of viral antigens. Previous reports have identified specific brain regions found to be positive for HSV-1 infection; however, to date, there has not been a concise investigation of the anatomical spread of HSV-1 and the brain regions consistently vulnerable to viral entry and spread. Understanding these region-specific differences in infection and immune response is crucial because it links HSV-1 infection to potential triggers for neurological and neurodegenerative diseases.

Heritability and genetic contribution analysis of structural-functional coupling in human brain

Abstract The flow of functional connectivity (FC) is thought to be supported by white matter structural connectivity (SC). While research on the correlations between SC and FC (SC-FC coupling) has progressed, the genetic implications of SC-FC coupling have not been thoroughly examined. Traditionally, SC-FC coupling investigations utilize predefined atlases. Here, we adopted an atlas-free SC-FC coupling built on the high-resolution white surface (the interface of white matter and gray matter) to uncover common genetic variations. Leveraging data from the Human Connectome Project, we demonstrated considerable heritability in areas within the early and intermediate visual cortex and across dorsal-attention, language, and somatomotor functional networks. We detected 334 genetic loci (spanning 234 cytogenetic bands) linked to SC-FC coupling (P < 1.26 × 10−11), notably in cingulo-opercular, somatomotor, and default mode networks. Using an external dataset from the Adolescent Brain Cognitive Development study, we confirmed 187 cytogenetic bands associated with SC-FC coupling across 22 brain regions (P < 1 × 10-5). Genetic correlation analyses revealed high genetic interrelatedness for SC-FC coupling in neighboring regions. Furthermore, it showed genetic correlations with a spectrum of complex traits, encompassing various neurological and psychiatric conditions. In essence, our study paves the way towards deciphering the genetic interplay between structural and functional connectivity of the brain.

Nifuroxazide rescues the deleterious effects due to CHCHD10-associated MICOS defects in disease models.

The identification of a point mutation (p.Ser59Leu) in the CHCHD10 gene was the first genetic evidence that mitochondrial dysfunction can trigger motor neuron disease. Since then, we have shown that this mutation leads to the disorganization of the MItochondrial contact site and Cristae Organizing System (MICOS) complex that maintains the mitochondrial cristae structure. Here, we generated yeast mutant strains mimicking MICOS instability and used them to test the ability of more than 1600 compounds from 2 repurposed libraries to rescue the growth defect of those cells. Among the hits identified, we selected nifuroxazide, a broad-spectrum antibacterial molecule. We show that nifuroxazide rescues mitochondrial network fragmentation and cristae abnormalities in CHCHD10S59L/+ patient fibroblasts. This molecule also decreases caspase-dependent death of human CHCHD10S59L/+ iPSC-derived motor neurons. Its benefits involve KIF5B-mediated mitochondrial transport enhancement, evidenced by increased axonal movement and syntaphilin degradation in patient-derived motor neurons. Our findings strengthen the MICOS-mitochondrial transport connection. Nifuroxazide and analogues emerge as potential therapeutics for MICOS-related disorders like motor neuron disease. Its impact on syntaphilin hints at broader neurological disorder applicability for nifuroxazide.

ORIENT Diet: A Potential Neuroprotective Dietary Pattern for Chinese Stroke High-Risk Population

ObjectivesGiven the particularity of Asian cultures and dietary habits, there is a need to establish a neuroprotective dietary pattern specially for the Asian stroke high-risk population, to prevent cognitive impairment. DesignA systematic review and cross-sectional study. Setting and ParticipantsChinese stroke high-risk population from the CIRCLE (NCT03542734) study. MethodsThe Oriental Intervention for Enhanced Neurocognitive healTh (ORIENT) diet was developed by replacing the western foods in the Mediterranean-DASH diet Intervention for Neurodegenerative Delay (MIND) diet with appropriate Asian alternatives, and refining the recommended intake frequency based on a systematic review, which examined Asian studies in the cognition-diet field. Cognitive impairment was defined as a global cognitive score ≥1 SD below the sample mean, based on the National Institute of Neurological Disorders and Stroke and Canadian Stroke Network vascular cognitive impairment battery. ResultsA total of 568 participants were finally included, with 325 men (57.2%) and a mean age of 60.5 ± 7.3 years. Participants in the highest tertile of ORIENT score had 60.5% lower odds of cognitive impairment than those in the lowest tertile (odds ratio = 0.395, 95% CI = 0.165-0.944, P = .044) when adjusted for demographic variables, lifestyle factors, and health status. Multiple linear regression analysis showed that each 1-point increase in ORIENT score was associated with 0.048 higher global cognitive score, 0.281 higher Mini-Mental State Examination score, and 0.344 higher Montreal Cognitive Assessment score. Conclusions and ImplicationsHigh adherence to ORIENT diet was associated with lower odds of cognitive impairment, providing a potential neuroprotective dietary pattern for Chinese stroke high-risk population.

Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).

Neurological disorders affecting speech production adversely impact quality of
life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking
devices and P300 spellers are slow and unnatural for these patients. An
alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech
characteristics, offering a more natural communication mechanism. This research
explores the feasibility of decoding speech features using non-invasive EEG. Nine
neurologically intact participants were equipped with a 63-channel EEG system
with additional sensors to eliminate eye artifacts. Participants read aloud sentences
displayed on a screen selected for phonetic similarity to the English language. Deep
learning models, including Convolutional Neural Networks and Recurrent Neural
Networks with and without attention modules, were optimized with a focus on
minimizing trainable parameters and utilizing small input window sizes for real-time
application. These models were employed for discrete and continuous speech decoding
tasks, achieving statistically significant participant-independent decoding performance
for discrete classes and continuous characteristics of the produced audio signal. A
frequency sub-band analysis highlighted the significance of certain frequency bands
(delta, theta, and gamma) for decoding performance, and a perturbation analysis
was used to identify crucial channels. Assessed channel selection methods did not
significantly improve performance, suggesting a distributed representation of speech
information encoded in the EEG signals. Leave-One-Out training demonstrated
the feasibility of utilizing common speech neural correlates, reducing data collection
requirements from individual participants.

BALB/c mice challenged with SARS-CoV-2 B.1.351 β variant cause pathophysiological and neurological changes within the lungs and brains.

Up to one-third of individuals suffering from acute SARS-CoV-2 infection with the onset of severe-to-mild diseases could develop several symptoms of neurological disorders, which could last long after resolving the infection, known as neuro-COVID. Effective therapeutic treatments for neuro-COVID remain unavailable, in part, due to the absence of animal models for studying its underlying mechanisms and developing medical countermeasures against it. Here, we explored the impact of SARS-CoV-2 infection on the well-being of respiratory and neurological functions of BALB/c mice by using a clinical isolate of β-variant, i.e. B.1.351. We found that this β-variant of SARS-CoV-2 primarily infected the lungs, causing tissue damage, profound inflammatory responses, altered respiratory functions and transient but significant hypoxia. Although live progeny viruses could not be isolated, viral RNAs were detected across many anatomical regions of the brains in most challenged mice and triggered activation of genes encoding for NF-kB, IL-6, IP-10 and RANTES and microglial cells. We noted that the significantly activated IL-6-encoded gene persisted at 4 weeks after infection. Together, these results suggest that this B.1.351/BALB/c model of SARS-CoV-2 infection warrants further studies to establish it as a desirable model for studies of neuropathogenesis and the development of effective therapeutics of neuro-COVID.

Practical supplements for prevention and management of migraine attacks: a narrative review

BackgroundMigraine is one of the most debilitating neurological disorders that causes frequent attacks of headaches and affects approximately 11% of the global population. Deficient or even insufficient levels of vital nutrients would increase the severity and frequency of migraine attacks. Therefore, we aimed to examine the practical supplements for the prevention and management of migraine attacks.MethodThis narrative review study was conducted by searching PubMed, ISI web of science, EMBASE, Google Scholar, and Scopus using the keywords of “dietary supplement” and “migraine” plus their MeSH terms. Original articles published in English language from their inception to July 27th, 2024, studies that investigated adult population (aged >18 years), and those assessing the impact of intended nutrient supplementation on clinical symptoms of migraine were included in the study.ResultOxidative stress and low intake of antioxidants would be risk factors for migraine attacks by inducing inflammation. The secretion of inflammatory cytokines, such as tumor necrosis factor (TNF)-a, would lead to neuroinflammation and migraine episodes by increasing the cellular permeability and interactions. Evidence also indicated a direct association between phases of migraine attacks and calcitonin gene-related peptide (CGRP), mitochondrial disorders, monoaminergic pathway, disruption in brain energy metabolism, and higher serum levels of glutamate and homocysteine. Therefore, supplementation with nutrients involved in mitochondrial function, brain energy metabolism, and even methyl donors would relieve migraine attacks.ConclusionEvidence indicated that supplementation with riboflavin, omega-3 fatty acids, alpha lipoic acid, magnesium, probiotics, coenzyme Q10, ginger, and caffeine would have favorable effects on migraine patients. However, more prospective studies are required to evaluate the effect of other nutrients on migraine patients.

Pharmacology and toxicology of Acetylcholinesterase: mechanisms, therapeutic potential, and environmental impact

Acetylcholinesterase (AChE) is a critical enzyme in the nervous system, responsible for terminating synaptic transmission by hydrolyzing acetylcholine. Due to its key role in neurotransmission, AChE and its inhibitors have become significant in treating neurological disorders such as Alzheimer’s, Parkinson’s, and Myasthenia Gravis. However, AChE inhibitors are also widely used in agriculture, posing potential environmental hazards as pesticides. This review provides a detailed analysis of the pharmacological role, therapeutic applications, and toxicological implications of AChE inhibitors. While these compounds offer therapeutic benefits, their environmental persistence and non-target toxicity raise ecological concerns. Balancing the dual impact of AChE inhibitors is vital to maximizing their benefits while minimizing risks.

Design of adaptive recommendation system for autism children using optimal feature selection-based adaptive dilated 1DCNN-LSTM with attention mechanism

One kind of neurological disorder is caused in the brain which is defined as Autism Spectrum Disorder (ASD). It has acquired the symptoms that appear in young children. In addition to that, it influences how the individual behaves and learns as well as communicates and interacts with others. More specifically, the term Autism is defined as a developmental disorder that impacts communication and social skills and it may vary from mental handicap cases to relieving superior cognitive abilities, intact, and the characteristic pattern of poor. Moreover, the school activities have acquired various difficulties to the given model that include changes in expected routines, intense sensory stimulation, noisy or disordered environments, and social interactions. Consequently, the conventional approaches face certain limitations like user privacy, scalability, and cold-start. Here, a novel suggestion system for autistic children is developed to detect distractions and anxious situations using deep learning and then treat the children based on their abilities. It has helped to prevent the risk to children. The data is given to the selection of the feature stage. The weight optimization is performed using the Modified Garter Snake Optimization Algorithm (MGSOA) during the selection of features. Then, the selected features are given to the Adaptive Dilated One Dimensional Conventional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) with Attention Mechanism termed AD-1DCNN + LSTM-AMfor detecting the autism disorder for children. Here, the parameter optimization is performed using MGSOA optimization. It effectively forecasts the symptoms in a short time. This optimization helps to provide reliable and flexible outcomes for the developed recommendation system for autistic children. The developed recommendation system for autistic children is compared to baseline techniques with efficacy metrics to visualize elevated results.

Epigenetic Explorations of Neurological Disorders, the Identification Methods, and Therapeutic Avenues

Neurodegenerative disorders are major health concerns globally, especially in aging societies. The exploration of brain epigenomes, which consist of multiple forms of DNA methylation and covalent histone modifications, offers new and unanticipated perspective into the mechanisms of aging and neurodegenerative diseases. Initially, chromatin defects in the brain were thought to be static abnormalities from early development associated with rare genetic syndromes. However, it is now evident that mutations and the dysregulation of the epigenetic machinery extend across a broader spectrum, encompassing adult-onset neurodegenerative diseases. Hence, it is crucial to develop methodologies that can enhance epigenetic research. Several approaches have been created to investigate alterations in epigenetics on a spectrum of scales—ranging from low to high—with a particular focus on detecting DNA methylation and histone modifications. This article explores the burgeoning realm of neuroepigenetics, emphasizing its role in enhancing our mechanistic comprehension of neurodegenerative disorders and elucidating the predominant techniques employed for detecting modifications in the epigenome. Additionally, we ponder the potential influence of these advancements on shaping future therapeutic approaches.

Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation

Abstract Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.

Spectral graph convolutional neural network for Alzheimer's disease diagnosis and multi-disease categorization from functional brain changes in magnetic resonance images

Alzheimer's disease (AD) is a progressive neurological disorder characterized by the gradual deterioration of cognitive functions, leading to dementia and significantly impacting the quality of life for millions of people worldwide. Early and accurate diagnosis is crucial for the effective management and treatment of this debilitating condition. This study introduces a novel framework based on Spectral Graph Convolutional Neural Networks (SGCNN) for diagnosing AD and categorizing multiple diseases through the analysis of functional changes in brain structures captured via magnetic resonance imaging (MRI). To assess the effectiveness of our approach, we systematically analyze structural modifications to the SGCNN model through comprehensive ablation studies. The performance of various Convolutional Neural Networks (CNNs) is also evaluated, including SGCNN variants, Base CNN, Lean CNN, and Deep CNN. We begin with the original SGCNN model, which serves as our baseline and achieves a commendable classification accuracy of 93%. In our investigation, we perform two distinct ablation studies on the SGCNN model to examine how specific structural changes impact its performance. The results reveal that Ablation Model 1 significantly enhances accuracy, achieving an impressive 95%, while Ablation Model 2 maintains the baseline accuracy of 93%. Additionally, the Base CNN model demonstrates strong performance with a classification accuracy of 93%, whereas both the Lean CNN and Deep CNN models achieve 94% accuracy, indicating their competitive capabilities. To validate the models' effectiveness, we utilize multiple evaluation metrics, including accuracy, precision, recall, and F1-score, ensuring a thorough assessment of their performance. Our findings underscore that Ablation Model 1 (SGCNN Model 1) delivers the highest predictive accuracy among the tested models, highlighting its potential as a robust approach for Alzheimer's image classification. Ultimately, this research aims to facilitate early diagnosis and treatment of AD, contributing to improved patient outcomes and advancing the field of neurodegenerative disease diagnosis.

Sleep-related epilepsies in the course of development in childhood

AbstractEpilepsy, as one of the most prevalent neurological diseases in childhood, has a strong reciprocal relationship with sleep. Sleep-associated epilepsy syndromes in childhood are mostly genetic and can be divided into (a) the group of self-limited focal epilepsies of childhood including self-limited epilepsy with autonomic seizures (SeLEAS) and self-limited epilepsy with centrotemporal spikes (SeLECTS) and (b) (non-self-limited) sleep-related hypermotor epilepsy (SHE). Sleep-accentuated (developmental and) epileptic encephalopathies (DEE/EE-SWAS, Landau–Kleffner syndrome [LKS]) are either genetic (possible transition from SeLEAS or SeLECTS) or structural, and they are characterized by continuous bilateral focal or generalized epileptic activity throughout the night with a clinical manifestation of stagnation or regression, in particular of cognition (verbal agnosia in LKS). Epilepsy syndromes with increased seizure frequency after sleep deprivation or with seizures in the transition to awakening include juvenile generalized epilepsy syndromes such as epilepsy with generalized tonic-clonic seizures alone (GTCA) or juvenile myoclonic epilepsy (JME), but also SeLECTS. Sleep is a very active process: Regeneration, reorganization, and consolidation of memory facilitate development and cognitive functioning. Epilepsy can alter sleep architecture and vice versa, which can appear as a vicious circle in epilepsies that are sleep related. Macrostructural elements of sleep such as sleep efficiency, sleep onset latency, wakefulness after sleep onset, REM and non-REM sleep fraction, as well as microstructural sleep elements such as slow-wave activity, slope of slow waves, cyclic alternating pattern (CAP), and physiological sleep figures, are important biomarkers with which to understand clinical symptoms such as cognitive stagnation and regression, to monitor treatment, but also to determine prognostic factors and will be an important tool for future studies.

Riluzole attenuates acute neural injury and reactive gliosis, hippocampal-dependent cognitive impairments and spontaneous recurrent generalized seizures in a rat model of temporal lobe epilepsy

BackgroundRiluzole exhibits neuroprotective and therapeutic effects in several neurological disease models associated with excessive synaptic glutamate (Glu) release. We recently showed riluzole prevents acute excitotoxic hippocampal neural injury at 3 days in the kainic acid (KA) model of temporal lobe epilepsy (TLE). Currently, it is unknown if preventing acute neural injury and the neuroinflammatory response is sufficient to suppress epileptogenesis.MethodsThe KA rat model of TLE was used to determine if riluzole attenuates acute hippocampal neural injury and reactive gliosis. KA was administered to adult male Sprague-Dawley (250 g) rats at 5 mg/kg/hr until status epilepticus (SE) was observed, and riluzole was administered at 10 mg/kg 1 h and 4 h after SE and once per day for the next 2 days. Immunostaining was used to assess neural injury (FJC and NeuN), microglial activation (Iba1 and ED-1/CD68) and astrogliosis (GFAP and vimentin) at day 7 and day 14 after KA-induced SE. Learning and memory tests (Y-maze, Novel object recognition test, Barnes maze), behavioral hyperexcitability tests, and spontaneous generalized recurrent seizure (SRS) activity (24-hour video monitoring) were assessed at 11–15 weeks.ResultsHere we show that KA-induced hippocampal neural injury precedes the neuroimmune response and that riluzole attenuates acute neural injury, microglial activation, and astrogliosis at 7 and 14 days. We find that reducing acute hippocampal injury and the associated neuroimmune response following KA-induced SE by riluzole attenuates hippocampal-dependent cognitive impairment, behavioral hyperexcitability, and tonic/clonic generalized SRS activity after 3 months. We also show that riluzole attenuates SE-associated body weight loss during the first week after KA-induced SE.DiscussionRiluzole acts on multiple targets that are involved to prevent excessive synaptic Glu transmission and excitotoxic neuronal injury. Attenuating KA-induced neural injury and subsequent microglia/astrocyte activation in the hippocampus and extralimbic regions with riluzole reduces TLE-associated cognitive deficits and generalized SRS and suggests that riluzole could be a potential antiepileptogenic drug.