Incurable Neurological Disorder Research Articles

LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.

Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.

Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer's disorder detection using EEG signals.

Alzheimer's disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer's disease more crucial since it lessens the amount of work for staff members and improves diagnostic outcomes. The major aim of this work is "to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal". Therefore, Dynamically Stabilized Recurrent Neural Network Optimized with Artificial Gorilla Troops espoused Alzheimer's Disorder Detection using EEG signals (DSRNN-AGTO-ADD) is proposed in this paper. Here, Dynamic Context-Sensitive Filter (DCSF) is considered to eliminate the noise, and interference from the EEG signal. Then Adaptive and Concise Empirical Wavelet Transform (ACEWT) is utilized to separate the filtered signals from the frequency bands, and to feature extraction from the EEG signals. Signal's characteristics, like logarithmic bandwidth power, standard deviation, variance, kurtosis, mean energy, mean square, norm are combined to ACEWT method to create feature vectors and enhance diagnostic performance. After that, the extracted features are fed to Dynamically Stabilized Recurrent Neural Network (DSRNN) for task classification. Weight parameter of DSRNN is enhanced using Artificial Gorilla Troops Optimization Algorithm (AGTOA). The proposed DSRNN-AGTOA-ADD algorithm is activated in MATLAB. The metrics including accuracy, specificity, sensitivity, precision, computation time, ROC are examined for AD diagnosis. The performance of the proposed DSRNN-AGTOA-ADD approach attains 12.98%, 5.98% and 23.45% high specificity; 29.98%, 23.32% and 19.76% lower computation Time and 29.29%, 8.365%, 8.551% and 7.915% higher ROC compared with the existing methods.

Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography

Parkinson’s disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multichannel, and nonlinear signal with noise that problematizes identifying PD symptoms. A few studies have employed fractal dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that k-nearest neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of 99.65±0.15%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99.65\\pm 0.15\\%$$\\end{document} for PD patients ON medication and 99.45±0.18%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99.45\\pm 0.18\\%$$\\end{document} for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB’s feature importance to generate brain topographic plots. This integration of explainable AI (XAI) enhanced the transparency and comprehensibility of our model’s classifications. Additionally, a comparison between the performance of FD and a few entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing fractal dimension as an effective way of extracting salient features from EEG signals.

Creatine supplementation with exercise reduces α-synuclein oligomerization and necroptosis in Parkinson's disease mouse model

Parkinson's disease (PD) is an incurable neurological disorder that causes typical motor deficits. In this study, we investigated the effects of creatine supplementation and exercise in the subacute 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD. We found that 2% creatine supplementation and/or exercise intervention for 4 weeks elicited neurobehavioral recovery and neuroprotective effects regarding dopaminergic cell loss in MPTP-treated mice; this effect implies functional preservation of dopaminergic cells in the substantia nigra, as reflected by tyrosine hydroxylase expression recovery. Creatine and exercise reduced necroptotic activity in dopaminergic cells by lowering mixed lineage kinase domain-like protein (MLKL) modification to active phenotypes (phosphorylation at Ser345 and oligomerization) and phosphorylated receptor-interacting protein kinase 1 (RIPK1) (Ser166-p) and RIPK3 (Ser232-p) levels. In addition, creatine and exercise reduced the MPTP-induced increase in pathogenic α-synuclein forms, such as Ser129 phosphorylation and oligomerization. Furthermore, creatine and exercise had anti-inflammatory and antioxidative effects in MPTP mice, as evidenced by a decrease in microglia activation, NF-κB-dependent pro-inflammatory molecule expression, and increase in antioxidant enzyme expression. These phenotypic changes were associated with the exercise/creatine-induced AMP-activated protein kinase (AMPK)/nuclear factor erythroid 2-related factor 2 (Nrf2) and sirtuin 3 (SIRT3)/forkhead box O3 (FoxO3a) signaling pathways. In all experiments, combining creatine with exercise resulted in considerable improvement over either treatment alone. Consequently, these findings suggest that creatine supplementation with exercise has anti-inflammatory, antioxidative, and anti-α-synucleinopathy effects, thereby reducing necroptotic cell death in a PD mouse model.

Automated Alzheimer's disease diagnosis using radiomics feature extraction on magnetic resonance images

The current study investigates automated Alzheimer disease diagnosis using radiomic feature extraction on magnetic resonance images. Alzheimer’s disease (AD) is a progressive, incurable neurological brain disorder. Early diagnosis of AD might prevent brain tissue damage and assist with proper treatment. Researchers examine numerous statistical and machine learning (ML) techniques for the detection of AD. Analyzing magnetic resonance imaging (MRI) is a traditional way of analyzing for AD in clinical examination. Diagnosis of AD is challenging because of the similarity in MRI statistics and standard healthy MRI information for elderly people. The application of DL to earlier diagnosis and automatic classification of AD is gaining immense popularity in recent times, as rapid development in neuroimaging techniques has generated large-scale multimodal neuroimaging datasets. This study develops a new Automated Alzheimer's Disease Diagnosis using Deep Learning Model (AADD-DLM) on MRI images. The presented AADD-DLM technique examines the MRI images to assist in the AD diagnostic process. In the presented AADD-DLM technique, three major processes are involved, namely skull stripping, segmentation, and feature extraction. Initially, the AADD-DLM technique uses the U-Net model for the skull stripping process, which enables the removal of the skull regions in the brain MRI. Next, the QuickNAT model is utilized for an effective brain MRI segmentation process. Moreover, the radiomics feature extraction approach is used to generate a useful set of feature vectors. For exhibiting the promising performance of the AADD-DLM technique, widespread experimentation analysis is made on the ADNI database. The optimized model achieves 99.6% accuracy in the ADNI database. The simulation outcomes revealed the improved effectiveness of the AADD-DLM technique over other recent approaches.

A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction.

Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.

Disocijativna amnezija s obilježjima fuge kod bolesnika s Huntingtonovom bolešću

Huntington's disease is a severe, incurable neurological disease characterised by motor, cognitive, andpsychological symptoms. This paper presents the case of a Huntington's disease patient with dissociativesymptoms. Clinical characteristics, genetic background, diagnostics, and guidelines for treatingHuntington's disease are shown in the paper. We emphasised the importance of knowing the genetic basisand testing the descendants of the patients. We highlighted the difference between behaviouralperseveration as a common psychomotor disorder in these patients and symptoms of obsessive-compulsivedisorders.Dissociative disorders may occur under extreme mental stress and exhaustion, such as when dealingwith a severe incurable illness. These disorders are not typical psychopathological phenomena forHuntington's disease, but conditions of hopelessness and despair after facing a severe diagnosis may leadto dissociative symptoms. Dissociative disorders as indicators of exhaustion of mental functioning requirecaution, especially given their high frequency of occurrence with suicidality as extreme exhaustion ofmental functioning.So far, treatment of Huntington's disease is only symptomatic, emphasising the relaxation of motorchoreatic movements and heterogeneous mental disorders. Antipsychotics that reduce choreatic movementsbut also affect psychotic symptoms, behavioural perseverations and mood stabilisation are used primarilyto relieve symptoms.As it is a progressive disease that ends in severe motor deficit, dementia and death, treating these patientswith a multidisciplinary team that includes neurological, psychiatric and palliative care, physical therapy,nursing, and social care is necessary. Psychotherapeutic and sociotherapeutic approaches make it easier forpatients and their families to cope with this severe disease. Genetic testing of the offspring is desirable todetermine the potential carrying of the mutated gene and its transmission to subsequent generations inwhich the disease appears earlier.

Effects of a Single Session of Whole Body Vibration Compared to Multiple Sessions—An Updated Review and Meta-Analysis

Parkinson’s disease is an incurable neurological disease. Only the symptoms can be treated with medication or exercise therapy. The present analysis is intended to show how whole-body vibration training affects the symptoms of Parkinson’s disease, distinguishing between acute and long-term effects. Methods: online databases (EMBASE, PubMed, PEDro) were searched for reviews, meta-analyses and new studies since the previous most recent review/meta-analysis. Studies with at least a medium methodological quality (PEDro score at least 5 points) were selected. Results were presented as forest plots that indicated standardized mean differences with 95% confidence interval. Results: Sixteen studies were found with a PEDro-score of at least 5 points. Of these, three studies were excluded from the qualitative analysis because the necessary data, such as standard deviation or control group results, were missing. The effect sizes are very mixed. In some parameters there is no effect, in others a very strong effect. The effects in the comparison between single and multiple treatments are similar. Discussion: The different effects may be partly due to the different vibration frequencies or sentence durations, as well as to different valid test procedures. Conclusions: Since the study situation still does not show clear results, further studies must follow that compare different frequencies, sentence durations and vibration types with each other, so that training recommendations can be given on this basis.

Current Diagnostic and Treatment Methods of Alzheimer's Disease: A Narrative Review.

To diagnose and predict the possibility of Alzheimer's or a different kind of dementia, medical professionals employ tests that look at a patient's mental competence; however, such methods are impracticable. A reliable diagnosis at the start of treatment is essential for therapy. Except in situations with apparent genetic variations, most Alzheimer's patients lack a known etiology. Therefore, every Alzheimer's patient receives the same treatment plan, regardless of the etiology, which may or may not be successful in slowing or preventing the disease's progression. Tau pathology is further complicated by the amyloid buildup that arises from the cellular phase of Alzheimer's disease (AD).Alzheimer's is a degenerative, diverse, complicated, and incurable neurological disorder primarily affecting elderly individuals. The currently accepted drugs available for treating AD, which involve cholinesterase inhibitors and N-methyl-D-aspartate (NMDA)-receptor antagonists only provide temporary relief from symptoms. The neurological disorder primarily affecting elderly individuals is degenerative, diverse, complicated, and incurable. Accurate diagnosis is the most essential prerequisite before beginning therapy. Most Alzheimer's patients' causes are still unclear, except for instances where hereditary variations have been noted. The gut microbiota composition significantly influences AD and any age-associated neurological illness. Therapies are very useful in improving the cognitive functions of AD. New microbiota-based therapy alternatives may now be available due to the more recent connection between the altered gut microbiome and neurodegeneration through the gut microbiota-brain axis.

An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.

Alzheimer's disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer's disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer's disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer's disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

Nanoparticle-Based Drug Delivery Systems: An Inspiring Therapeutic Strategy for Neurodegenerative Diseases.

Neurodegenerative diseases are common, incurable neurological disorders with high prevalence, and lead to memory, movement, language, and intelligence impairments, threatening the lives and health of patients worldwide. The blood-brain barrier (BBB), a physiological barrier between the central nervous system and peripheral blood circulation, plays an important role in maintaining the homeostasis of the intracerebral environment by strictly regulating the transport of substances between the blood and brain. Therefore, it is difficult for therapeutic drugs to penetrate the BBB and reach the brain, and this affects their efficacy. Nanoparticles (NPs) can be used as drug transport carriers and are also known as nanoparticle-based drug delivery systems (NDDSs). These systems not only increase the stability of drugs but also facilitate the crossing of drugs through the BBB and improve their efficacy. In this article, we provided an overview of the types and administration routes of NPs, highlighted the preclinical and clinical studies of NDDSs in neurodegenerative diseases, and summarized the combined therapeutic strategies in the management of neurodegenerative diseases. Finally, the prospects and challenges of NDDSs in recent basic and clinical research were also discussed. Above all, NDDSs provide an inspiring therapeutic strategy for the treatment of neurodegenerative diseases.

The Use of Stem Cell Therapy in the Treatment of Autism Spectrum Disorder

Research on the use of stem cell therapy in regenerative medicine has shown great potential in the treatment of neurological disorders. Specifically, mesenchymal stem cells, adult stem cells from the umbilical cord and embryonic stem cells have been used in both animal models and clinical trials to treat autism spectrum disorder. Autism Spectrum Disorder (ASD) is an incurable neurological disorder characterized by behavioral and neuroanatomical abnormalities, commonly co-occurring with other neurological disorders including anxiety, depression and obsessive-compulsive disorder. Stem cell therapies are currently being researched to alleviate the behavioral tendencies typically seen in individuals with ASD.

Nanotherapeutic and Stem Cell Therapeutic Strategies in Neurodegenerative Diseases: A Promising Therapeutic Approach.

Neurodegeneration is characterized by progressive, disabling, and incurable neurological disorders with the massive loss of specific neurons. As one of the most promising potential therapeutic strategies for neurodegenerative diseases, stem cell therapy exerts beneficial effects through different mechanisms, such as direct replacement of damaged or lost cells, secretion of neurotrophic and growth factors, decreased neuroinflammation, and activation of endogenous stem cells. However, poor survival and differentiation rates of transplanted stem cells, insufficient homing ability, and difficulty tracking after transplantation limit their further clinical use. The rapid development of nanotechnology provides many promising nanomaterials for biomedical applications, which already have many applications in neurodegenerative disease treatment and seem to be able to compensate for some of the deficiencies in stem cell therapy, such as transport of stem cells/genes/drugs, regulating stem cell differentiation, and real-time tracking in stem cell therapy. Therefore, nanotherapeutic strategies combined with stem cell therapy is a promising therapeutic approach to treating neurodegenerative diseases. The present review systematically summarizes recent advances in stem cell therapeutics and nanotherapeutic strategies and highlights how they can be combined to improve therapeutic efficacy for the treatment of neurodegenerative diseases.

FERM domain containing kindlin 1 knockdown attenuates inflammation induced by intracerebral hemorrhage in rats via NLR family pyrin domain containing 3/nuclear factor kappa B pathway.

Intracerebral hemorrhage (ICH) is an incurable neurological disease. Microglia activation and its related inflammation contribute to ICH-associated brain damage. FERM domain containing kindlin 1 (FERMT1) is an integrin-binding protein that participates in microglia-associated inflammation, but its role in ICH is unclear. An ICH model was constructed by injecting 50 µl of autologous blood into the bregma of rats. FERMT1 siRNA was injected into the right ventricle of the rat for knockdown of FERMT1. A significant striatal hematoma was observed in ICH rats. FERMT1 knockdown reduced the water content of brain tissue, alleviated brain hematoma and improved behavioral function in ICH rats. FERMT1 knockdown reduced microglia activity, inhibited NLR family pyrin domain containing 3 (NLRP3) inflammasome activity and decreased the expression of inflammatory factors including IL-1β and IL-18 in the peri-hematoma tissues. BV2 microglial cells were transfected with FERMT1 siRNA and incubated with 60 µM Hemin for 24 h. Activation of NLRP3 inflammasome induced by hemin were reduced in microglia when FERMT1 was knocked down, leading to decreased production of inflammatory factors IL-1β and IL-18. In addition, knockdown of FERMT1 prevented the activation of nuclear factor kappa B (NF-κB) signaling pathway in vivo and in vitro. Our findings suggested that down-regulation of FERMT1 attenuated microglial inflammation and brain damage induced by ICH via NLRP3/NF-κB pathway. FERMT1 is a key regulator of inflammatory damage in rats after ICH.

Attitudes of non-physician health workers working in the Emergency Department towards euthanasia, death, and the terminally patient

<b>Aim: </b>The use of the right to die in the center of the individual's own decision is called euthanasia. This decision, was evaluated from legal, religious, and medical perspectives. In different countries applied euthanasia, which can be performed actively or passively. In our study, we planned to investigate the perspectives of healthcare professionals working in the emergency department about euthanasia and their thoughts on diseases that can be applied to euthanasia.<br /> <b>Material and methods: </b>A survey was conducted from June to October 2022 on non-physician health workers working in the Emergency department. A questionnaire including demographic data, professional knowledge, and Attitude Scale towards Euthanasia, Death, and the Terminally Patient was administered to the healthcare professionals who agreed to participate in the study. The obtained data were analyzed.<br /> <b>Results</b>: In the study, the feedback of 60 participants, 37 of whom were women, was evaluated. The mean age of the entrants was found to be 39.07±10.11 years. 60% of the participants had received cardiopulmonary resuscitation training in the past year. 70% of the participants stated that they could be euthanized for coma, 38.33% for severe disability, and 36.67% for severe and incurable neurological diseases.<br /> <b>Conclusion</b>: In the process of euthanasia, which does not have a legal infrastructure in our country, different perspectives are seen from different departments of health services.<br /> <b>Key words:</b> emergency department, euthanasia, non-health workers

Towards a telehealth infrastructure supported by machine learning on edge/fog for Parkinson's movement screening

Approximately 10 million people worldwide live with Parkinson's disease (PD), a progressive and incurable neurological movement disorder. Symptomatic treatment is available for PD but requires patients to make periodic clinic visits (2–3 times per year) for symptom assessment. Advanced telehealth technologies can enhance clinical care for PD but warrant an Internet-of-Things-based (IoT) infrastructure that can enable symptom monitoring in out-of-clinic settings, such as homes. In this paper, we present an edge and Fog device-based IoT framework and a machine learning-based telehealth infrastructure that can detect and classify hand movement tasks based on a clinical test (UPDRS) for remote symptom assessment. We used a pair of smart gloves integrated with finger flex sensors, an inertial measurement unit (IMU), and a wireless embedded system (i.e., an edge device) to record the hand movements. The edge device (ESP32) detected the activity on the edge node and transmitted the data to the Fog node for classification. The Fog node (Raspberry pi) hosted the Machine Learning (ML) based activity classification models to classify UPDRS-based hand movement tasks. In this paper, we present the development of edge-fog-supported ML infrastructure. To develop ML models, we utilized the data of 9 participants (five healthy and four people with PD) who performed hand movements tasks while wearing the smart gloves. We developed and tested different classification models such as K-nearest neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT) to identify which one fits best for such an edge-fog infrastructure for remote symptom assessment. Results showed that the SVM model outperformed others by giving 94% training, 94% testing, and 93% validation accuracy with a mean inference time of 560 μs on the Fog node. Our preliminary results are promising to further our research in deploying the edge-fog-driven ML-based telehealth infrastructure in real-world settings. • Development of Edge-Fog-supported telehealth IoT infrastructure. • Development and assessment of Machine Learning classifiers for hand movement activity classification. • Deployment and evaluation of the ML classification models on the Fog infrastructure. • Demonstration of how an edge-fog computing architecture can be useful for telehealth applications, including movement assessment, symptom tracking, and symptom management.

Acat1/Soat1 knockout extends the mutant Npc1 mouse lifespan and ameliorates functional deficiencies in multiple organelles of mutant cells

Multiple membrane organelles require cholesterol for proper function within cells. The Niemann-Pick type C (NPC) proteins export cholesterol from endosomes to other membrane compartments, including the endoplasmic reticulum (ER), plasma membrane (PM), trans-Golgi network (TGN), and mitochondria, to meet their cholesterol requirements. Defects in NPC cause malfunctions in multiple membrane organelles and lead to an incurable neurological disorder. Acyl-coenzyme A:cholesterol acyltransferase 1 (ACAT1), a resident enzyme in the ER, converts cholesterol to cholesteryl esters for storage. In mutant NPC cells, cholesterol storage still occurs in an NPC-independent manner. Here we report the interesting finding that in a mutant Npc1 mouse (Npc1nmf), Acat1 gene (Soat1) knockout delayed the onset of weight loss, motor impairment, and Purkinje neuron death. It also improved hepatosplenic pathology and prolonged lifespan by 34%. In mutant NPC1 fibroblasts, ACAT1 blockade (A1B) increased cholesterol content associated with TGN-rich membranes and mitochondria, while decreased cholesterol content associated with late endosomes. A1B also restored proper localization of syntaxin 6 and golgin 97 (key proteins in membrane trafficking at TGN) and improved the levels of cathepsin D (a key protease in lysosome and requires Golgi/endosome transport for maturation) and ABCA1 (a key protein controlling cholesterol release at PM). This work supports the hypothesis that diverting cholesterol from storage can benefit multiple diseases that involve cholesterol deficiencies in cell membranes.

Фізична реабілітація та відновлення при неврологічних захворюваннях

The author proved that the problem of neurological diseases is relevant despite the high development of medicine, because in conditions of stress, constant fatigue and poor ecology, we see the growth of patients with nervous disorders. Every year, more and more people of all ages turn to doctors with symptoms that indicate complex neurological diseases. Neurology includes a large number of diseases that can occur after viral diseases or due to improper lifestyle. The most common neurological diseases that cause a large number of deaths in Ukraine are Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis. Some neurological diseases are more treatable and easier to tolerate, the progression and development of which can be stopped, while others are severe, which official medicine defines as incurable and lead to disability. It is important to seek the help of a specialist in time to correctly diagnose the disease and avoid unforeseen complications. For each patient it is necessary to select an individual treatment program that depends on the severity of the disease and diagnosis. Today, such a complex and currently incurable neurological disease as multiple sclerosis is becoming more common. This is a chronic autoimmune disease in which the myelin sheath of the nerve fibers of the brain and spinal cord is damaged and thus the functioning of the nervous system is disrupted. Today, this disease is one of the leading causes of disability in young patients. Occurs for unknown reasons and is constantly progressing, leading to serious physical changes and disability. The author argues that physical rehabilitation is a natural and extremely effective method of treating neurological diseases, including multiple sclerosis.

HUC-MSC-mediated recovery of subacute spinal cord injury through enhancing the pivotal subunits β3 and γ2 of the GABAA receptor.

Rationale: Spinal cord injury (SCI) remains an incurable neurological disorder leading to permanent and profound neurologic deficits and disabilities. Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) are particularly appealing in SCI treatment to curtail damage, restore homeostasis and possible neural relay. However, the detailed mechanisms underlying hUC-MSC-mediated functional recovery of SCI have not been fully elucidated. The purpose of our current study is to identify novel therapeutic targets and depict the molecular mechanisms underlying the hUC-MSC-mediated recovery of subacute SCI.Methods: Adult female rats suffering from subacute incomplete thoracic SCI were treated with intrathecal transplantation of hUC-MSCs. The beneficial effects of hUC-MSCs on SCI repair were evaluated by a series of behavioral analyses, motor evoked potentials (MEPs) recording of hindlimb and immunohistochemistry. We carried out extensive transcriptome comparative analyses of spinal cord tissues at the lesion site from the subacute phase of SCI (sub-SCI) either treated without (+PBS) or with hUC-MSCs (+MSC) at 0 (sub-SCI), 1, 2, and 4 weeks post-transplantation (wpt), as well as normal spinal cord segments of intact/sham rats (Intact). Adeno-associated virus (AAV)-mediated neuron-specific expression system was employed to functionally screen specific γ-aminobutyric acid type A receptor (GABAAR) subunits promoting the functional recovery of SCI in vivo. The mature cortical axon scrape assay and transplantation of genetically modified MSCs with either overexpression or knockdown of brain-derived neurotrophic factor (BDNF) were employed to demonstrate that hUC-MSCs ameliorated the reduction of GABAAR subunits in the injured spinal cord via BDNF secretion in vitro and in vivo, respectively.Results: Comparative transcriptome analysis revealed the GABAergic synapse pathway is significantly enriched as a main target of hUC-MSC-activated genes in the injured spinal cord. Functional screening of the primary GABAAR subunits uncovered that Gabrb3 and Garbg2 harbored the motor and electrophysiological recovery-promoting competence. Moreover, targeting either of the two pivotal subunits β3 or γ2 in combination with/without the K+/Cl- cotransporter 2 (KCC2) reinforced the therapeutic effects. Mechanistically, BDNF secreted by hUC-MSCs contributed to the upregulation of GABAAR subunits (β3 & γ2) and KCC2 in the injured neurons.Conclusions: Our study identifies a novel mode for hUC-MSC-mediated locomotor recovery of SCI through synergistic upregulation of GABAAR β3 and γ2 along with KCC2 by BDNF secretion, indicating the significance of restoring the excitation/inhibition balance in the injured neurons for the reestablishment of neuronal circuits. This study also provides a potential combinatorial approach by targeting the pivotal subunit β3 or γ2 and KCC2, opening up possibilities for efficacious drug design.

How can libraries support dementia friendly communities? The study into perceptions and experiences of Croatian public librarians

Alzheimer’s Disease (AD), the most common cause of dementia, is an incurable neurological disease with an unknown cause. Since AD is not only social and health challenge but also an economic and fiscal burden and its prevalence is expected to grow exponentially as world population gets older, in 2012 the World Health Organization (WHO) and Alzheimer’s Disease International (ADI) recognized dementia as a global public health priority. Although librarians have not generally been recognized as professionals caring for persons with dementia, recent research indicates that public libraries can contribute to the development of dementia friendly communities by supporting social inclusion, self-efficacy and capacity building of persons with AD, and by providing support to their informal caregivers (CGs). The research reported in this paper aims to examine the role of Croatian public libraries in building a dementia friendly communities, in a country where information, health and social care systems for D patients and their CGs are not sufficiently developed (Rušac, 2016; Petr Balog et al., 2020). With the help of quantitative (online survey) methodology authors explore how Croatian public libraries tailor their spaces, collections, programs and services in order to enhance the everyday experience of persons with AD and their CGs.