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Carpal tunnel syndrome is a neurological disorder resulting in loss of sensory and motor functions in the hand. The median nerve passing through the carpal tunnel is squeezed by the surrounding tissues. In contrast to finite-element modeling, existing clinical methods for determining carpal tunnel syndrome do not demonstrate pressure in the median nerve. Patient-specified knowledge of pressure in the median nerve is a key factor in carpal tunnel syndrome. MRI and CT scans are used to build three-dimensional geometry of the carpal tunnel and index finger. The stress-strain state is obtained by finite-element modeling. The behavior of the soft tissues are considered hyperelastic and are described by the first-order Ogden constitutive model. The calculations are performed in the ANSYS software package. The results showed that with a slight flexion of the index finger, the median nerve experiences a pressure of 50 Pa. It has also been shown that dissection of the transverse ligament of the hand can lead to an increase in pressure in the median nerve up to 70 Pa. The model built in this study will make it possible to assess the influence of many other mechanical factors on median nerve compression. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological condition that causes damage to the nervous system and causes sudden seizures. This article covers a new prototype device that detects seizures using multiple sensors, such as heart rate, accelerometer, and galvanic skin response (GSR). The device can precisely determine whether a seizure has occurred because of the integration of data from many sensors. The heart rate sensor monitors heart rate fluctuations, the accelerometer detects movement and falls during a seizure, and the GSR sensor measures stress and emotional responses. This device aims to improve the accuracy and speed of seizure detection. The study also offers a smartphone app that helps individuals care for their illnesses better. [ABSTRACT FROM AUTHOR]

Parkinson disease has more than 10 million cases worldwide, making it the second most prevalent neurological disorder overall. A decline in motor and cognitive function is the main characteristic of Parkinson's disease. No one test can be utilized to establish a diagnosis. Instead, health care providers must do a thorough clinical inquiry of the patient's medical history. But this diagnostic strategy is completely off. According to studies, only 53% of cases with early diagnosis—defined as five years or less of symptoms—are correct. Even if it's not much better than winging it, therapeutic success depends on early diagnosis. Although research is ongoing and medications or surgery can assist, as of right now, there is no known cure for Parkinson disease. Frequently result in notable improvements with motor symptoms. One of the deadliest illnesses is Parkinson's disease. Therefore, detecting it earlier could assist avoid or lessen the impacts. A person's Parkinson disease status can be determined by. A person's Parkinson disease status can be determined by: classification regarding machine learning methods are utilized. These algorithms include decision tree, logistic regression and other "Ensemble" learning techniques, which aim to increase accuracy by merging many models. The implementation of the machine learning model can greatly enhance the Parkinson disease diagnosing process. This study shows that, in comparison to other classification algorithms, the ensemble methods The high test accuracy percentage of 95.5 percent was attained with the Random Forest classification algorithm. As this project is related to health, we made sure that we selected the correct algorithm that gave the low false positive and False Negative values by using confusion matrix. [ABSTRACT FROM AUTHOR]

With more than 10 million cases globally, Parkinson disease is the second most common neurological disorder. Parkinson's disease is generally distinguished by a decline in motor and cognitive function. There isn't a single test that can be used to make a diagnosis. Instead, medical professionals must do a careful clinical investigation of the patient's medical background. But this diagnostic approach is incredibly incorrect. According to National Institute of Neurological Disorders research, the accuracy of an early diagnosis—defined as five years or less of symptoms—is only 53%. Although this isn't much better than winging it, prompt diagnosis is essential for successful treatment, Parkinson's disease has no known cure, but early diagnosis and treatment can help manage symptoms and halt the illness's development. Medication, deep brain stimulation, and physical therapy are possible forms of treatment. In recent years, research has focused on developing new technologies and methods for early detection and diagnosis of Parkinson's disease, including the use of machine learning algorithms to analyze data from wearable devices and other sources. The hope is that by detecting the disease earlier, interventions can be implemented earlier and potentially improve outcomes for patients. To determine whether a person is affected with Parkinson disease or not, classification regarding machine learning methods is utilized. These algorithms include Random Forest, Decision trees, XGB classifier and other "Ensemble" learning techniques, which aim to increase accuracy by merging many models. The implementation of the machine learning model can greatly enhance the Parkinson disease diagnosing process. This study shows that, in comparison to other classification algorithms, the ensemble approaches Random Forest classification algorithm achieved the high-test accuracy rate of 97.0%. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurological condition that may severely impair a patient's quality of life and has substantial repercussions for these sufferers. For successful care, early diagnosis is very necessary. In this study, we investigate the possibility that contemporary machine learning methods might improve the early identification of Parkinson's disease (PD). The diagnosis of Parkinson's disease has traditionally been based on clinical evaluations, imaging, and biomarkers. The diagnosis of Parkinson's disease (PD) has been revolutionized thanks to recent breakthroughs in machine learning, with an emphasis on one-of-a-kind methods such as speech and gait pattern analysis. The accuracy of speech analysis can reach 99.5%, while the accuracy of gait pattern analysis can approach 96%. The technique includes the steps of data collecting, training and testing, the application of the XGBoost algorithm, and the creation of code. The correctness and dependability of the system can only be guaranteed by exhaustive testing, which should include component, integration, functional, and performance testing. Diagnostic findings, performance indicators, and data visualizations are all generated by the system. Even if the accuracy of the present model is 73.8%, it is still feasible to make additional improvements using datasets that are both bigger and more consistent. The findings of this study highlight the potential of machine learning in the early diagnosis of PD, bringing hope for better patient treatment and outcomes. Utilizing the available technology and data, it sets the path for a more promising future in the diagnosis and treatment of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Motor abilities, communication, and other functions are all negatively impacted by Parkinson's disease, a neurological disorder that affects the central nervous system. Motor neuron weakening and tremors ranging from moderate to severe are symptoms of Parkinson's disease. People with Parkinson's disease might benefit from our clever approach, which we present in this research. A toothbrush is one of the most important tools they use every day. Even if they could use the toothbrush, the person with PD would still have trouble using it since they rip their gums when brushing. Patients with PD may benefit from using a toothbrush that stabilises itself. To create this model, one may use a sensor that combines gyroscopes and accelerometers to measure the speed and direction of roll or shaking of the device's handle. Maintaining the toothbrush's horizontal position with the teeth of PD patients is the goal of this configuration. Vibration treatment is also available in addition to this auxiliary equipment. We are using a WiFi Module to store the values of the sensor parameters in the cloud platform, and patients may simply toggle between modes with the provided keypad (Internet of Things). [ABSTRACT FROM AUTHOR]

Cerebral palsy is a neurological disorder characterized by impaired posture and movement. It stems from brain lesions and poses significant challenges to individuals. Rehabilitation stands as a cornerstone in the management of this disorder, demanding intensive therapeutic interventions across various tasks. A potential avenue in this endeavor involves the utilization of exoskeletons—devices capable of being worn by patients to augment or fully execute specific bodily movements. Consequently, the field of exoskeleton design has garnered paramount research interest. This paper is focused on the development of an exoskeleton tailored for cerebral palsy patients exhibiting lower limb impairments, with a specific emphasis on enhancing ambulatory capabilities. The proposed exoskeleton aims to facilitate lower limb training for ambulation. Presented herein is a virtual model of the tangible system, evaluated within a simulation environment to ascertain adherence to requisite specifications. While prevailing exoskeletons often target individual users, this study endeavors to devise a height-independent and, thus, user-independent exoskeletal model. Extensive analysis is conducted to validate the real-time feasibility of the proposed model, employing simulation methodologies such as Model-in-Loop and Software-in-Loop simulations. [ABSTRACT FROM AUTHOR]

The mortality rate in the intensive care unit (ICU) is still high. Several risk factors for death of patients in the ICU need to be identified so that further treatment planning can be carried out to reduce mortality. This study used cross-sectional retrospective data from the medical records of patients who died in the ICU at Jogja Hospital in 2022. Data analysis used multivariable logistic regression analysis with a significance level of p <0.05 to determine the risk factors that influence patient mortality in the ICU. In this study, 107 subjects were treated in the ICU in 2022, consisting of 48 women and 59 men. The number of subjects who died was 50 (46.7%), and 57 (53.3%) were alive. Risk factors for ICU death include sepsis/septic shock, hypertension, hypovolemic shock, diabetes mellitus, kidney failure (including CKD), heart problems (including CHF, IHD, HHD, STEMI, UAP and cardiogenic shock), pulmonary disorders (including pneumonia, ARDS, and edema), pleural effusion, central nervous system disorders (including hemorrhagic and non-hemorrhagic stroke, cerebral infarction, and encephalitis), status epilepticus, and hepatic disorders (including liver cirrhosis and hepatopathy), abnormal white cell count, anemia, thrombocytopenia, hypo albumin, electrolyte disturbances, acidosis, use of mechanical ventilators, and hemodialysis. From the multivariable logistic regression test it was found that septic shock, hypertension, kidney failure, neurological disorders, hypo albumin, and use of mechanical ventilators were risk factors for death in the ICU (p<0.05). [ABSTRACT FROM AUTHOR]

Researchers have thoroughly researched neurological disorders from a general cause perspective, in which symptoms are viewed as separate indicators of an underlying illness. The perspective of causal mechanisms, in contrast, seeks to understand the relevance of the connections between casual and depression symptoms. In the present study, to analyze the relationships between depression and the control sample, we used network analysis from causal processes. Major depressive disorder (MDD), although its etiology and function remain unknown, is a leading cause of disability worldwide. The goal of our research was to identify MDD-related hub genes and to explain the underlying mechanisms. Interactomics research was carried out via clueGo to identify essential gene modules and hub genes associated with the MDD, which revealed gene ontology and gene cluster pathways using multiple sources, such as the STRING interaction database, DAVID, and KEGG. [ABSTRACT FROM AUTHOR]

Aim: The primary objective of this research is to increase the accuracy of detecting brain tumors using the Random Forest algorithm and to compare it to the thresholding method. Methods: There are two separate studies that are conducted in this research, the first is the Random Forest algorithm, and the second is a thresholding method. Analysis is conducted with the 36 magnetic resonance images (MRI) that are available, of which 90 % are dedicated to training and 10 % are used to test the effectiveness of a brain tumor. The total number of samples is 36 (18 samples per group), and the pre-test probability of the total number is 80 %, alpha is 0.05, and 95 % confidence intervals. Results : The accuracy of 81.69% and 75.81% are attained using the random forest and thresholding methods respectively. The two classes have a significant difference, with p<0.05 Conclusion: From the study, it is evident that the degree of success in recognizing brain disorders of neurological origin using the Random Forest classifier is greater than the thresholding method. [ABSTRACT FROM AUTHOR]

One neurotransmitter, glutamate, is crucial for the development and maintenance of synapses as well as for the processes of consciousness, memory, and learning. Abnormal levels of glutamate in the body can lead to various risks of neurological diseases. The existing tools for detecting glutamate levels have several drawbacks. They are time-consuming and expensive. Besides that, less research or development has been done on glutamate biosensors than on other types of biosensors. In this review, the development of electrochemical glutamate biosensors was discussed. A review of the analyte, enzymes used, immobilization techniques, materials used, and analysis findings regarding linear range, detection limit, and sensitivity has been performed in order to develop a glutamate biosensor. The non-enzymatic electrochemical glutamate sensor can continue to be developed for promising enzyme-free glutamate biosensors. [ABSTRACT FROM AUTHOR]

Mental illnesses are usually in the domain of neurological disorders but unlike most, some can be treated, though not permanently, without the use of medication - this is where trained psychotherapists come in. Trained professionals have been studying and dissecting the understanding patterns and manipulative powers of humans for many years. This project harnesses the power of Natural Language Processing using pretrained models to understand and generate conversational text. Artificial Intelligence (AI) has raised many expectations in playing a major role in mental healthcare in the times to come. It has become a huge domain which often coincides with psychotherapy. To be able to provide proper clinical practice and safeguard ethical and social impacts of the growing applications of AI in psychological health need to be addressed on identification. Research in this field can help make exponential leaps in terms of clinical relevance for applications in therapeutic services. [ABSTRACT FROM AUTHOR]

Mealtime is one of the pleasures of daily life and an important factor in ensuring the quality of life. Aging and brain and neurological diseases often complicate eating. In particular, the ability to swallow food directly, such as in dysphagia, has a significant impact on eating. In this study, a new observation method was proposed for the cricopharyngeal muscles, which are important for swallowing. Experiments were conducted on two healthy participants. A muscle deformation sensor was used to observe the cricopharyngeal muscles. The results showed that the contraction and relaxation of the cricopharyngeal muscle during swallowing can be visualized noninvasively and easily using muscle deformation sensors in healthy subjects. Based on this experiment, the possibility of its clinical application in patients with dysphagia is suggested. [ABSTRACT FROM AUTHOR]

Household air pollution was responsible for an estimated 3.2 million deaths per year in 2020, including over 237,000 deaths of children under the age of 5. A large number of these death cases was particularly recorded in developing countries where many people rely heavily on biomass for energy. Burning biomass emits carbon monoxide and other pollutants resulting in indoor air pollution, exacerbations of asthma, hospitalizations for heart attacks and respiratory illness, birth defects, neurological diseases, and even mortality, which are all brought on by indoor air pollution. Because women and children typically do most of the cooking, they are most affected by indoor air pollution. In this research, an active sampling technique was adopted in estimating the amount of three major criteria gaseous pollutants (CO, H2S, and SO2) in the air in rural household kitchens within the Jos metropolis. The Attair 5X gas detector was used. The power button was pressed and the equipment was allowed to initialize for few minutes while the readings were taken downwind in-situ at a distance of 1 m, 2 m, 3 m, 4 m, and 5 m respectively from the emission source at the expiration of one (1) minute for each distance to check the impact of emissions on the environment and people in such areas. The results obtained shows that CO, H2S, and SO2 were higher from firewood emission sources when compared with charcoal emission sources from the 14 different rural kitchens in the Bauchi ring road, Jos, Plateau State, Nigeria. Hence, this study serves as a ready reference for environmentalists to make target decisions on air pollution reduction. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the major neurological diseases affecting the nervous system of human body. Till now, there is no proper clinical examination that can diagnose a fully affected PD patient. But, the findings and reports states that the PD patients face disastrous changes in their handwriting. Hence, machine learning experts and research people have proposed different macrograph and computer vision (CV) based methods. Currently, it can take months to get an efficient and proper PD diagnosis, and symptoms that are to be noted and monitored effectively. Even on that note the probability of an improper diagnosis is approx. 20 Percent. I have used the Parkinson's disease handwritten dataset and voice dataset. The results confirmed that handwriting is relevant in diagnosing and monitoring PD. Another set of voice dataset has been used to compare it with handwritten dataset. This is an attempt to find the disease as soon as possible by increasing the accuracy of previous results on the same by other researchers. Here I have used classifiers like LDA, KNN, SVM, RF and DT that can predict PD disease, from that SVM has shown greater result in both dataset but was giving greater accuracy in handwritten dataset. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a complex neurological condition that manifests as a spectrum of symptoms at varying levels of severity. Insufficient data and heterogeneous characteristics of ASD are the primary causes of it being a complex, challenging, and intriguing field of research. ASD is declared one of the fastest-growing mental disorders affecting the normal life of subjects at various levels of severity and stages of age. Recent research work observed a significant change in brain structure, functional connectivity, and network using neuroimaging resources. Each autistic brain is as unique as a fingerprint for typically developed subjects. Magnetic Resonance Imaging (MRI) is accepted as an excellent diagnostic technology for numerous disorders with a satisfactory amount of information by medical experts. Cognitive deficits brain MRI modalities contain microscopic information, which is time-consuming and needs experts to interpret. Artificial intelligence (AI) strategies (Machine Learning and Deep Learning) are implemented with various imaging modalities to decrypt the information for diagnosis and to support computer-added solutions for appropriate treatment. The research aims to discover the various evolutionary impacts of artificial intelligence for the diagnosis of Autism syndrome disorder using neuroimaging. To automate the diagnosis using artificial intelligence methodologies, medical imaging has proved to be of immense use. Though neuroimaging and AI produced satisfactory diagnostic solutions for many mental disorders, research is required to explore the autistic brain for more neuroimaging information to be used for further investigation. Some of the Internet of Things (IoT) solutions for detection and training are also invented but not with the use of Neuroimaging. Autism is a neurological condition that affects the brain, and hence more research is advised using imaging and AI techniques to support the community to enjoy a normal life. [ABSTRACT FROM AUTHOR]

The performance during neurological disease recovery outcome assessment was usually affected by the patient ability to manipulate muscles to perform necessary movements. As the assessments were the gold standards to evaluate the recovery, the muscle activity during the assessments was an important domain to be studied. In this work, the trunk muscle activities of three healthy subjects during the outcome assessments were investigated. Electromyography (EMG) electrodes were placed at the erector spinae, external oblique, and rectus abdominis. The EMG readings were recorded during Berg Balance Scale assessment, Trunk Impairment Scale assessment, and 10 Meter Walk Test. The mean and standard deviation of muscle activity percentage were determined. The results showed more than 50 % mean muscle activation percentages could be generated by one or more trunk muscles in all tasks. In the future work, the results of the healthy subjects will be compared with that of the patients diagnosed with neurological diseases. [ABSTRACT FROM AUTHOR]

Neurotransmitters play a crucial role in neurological functions. Neural activity is significantly impacted by the balance of neurotransmitters in different parts of the brain. In order to comprehend many complex multi-factorial neurological diseases, mathematical modelling can be helpful. Neurotransmitter release, diffusion, and intake are studied rigorously nowadays in various computational models of brain disorders. The main focus of this work is on dopamine, a neurotransmitter that plays an important role associated with the brain's reward system. Dopamine-mediated neurodegenerations like Parkinson's disease (PD) and Schizophrenia have been studied recently using computational approaches. The current work, presented here uses mathematical models to analyse the impact of dopamine levels on specific brain networks leading to diseases such as PD. In this work, neural dynamics in the normal and parkinsonian states are studied in detail with the help of biologically inspired Basal Ganglia (BG) models. The simulations illustrate the behaviour of BG neurons under altered dopamine levels leading to normal or disease states. An extended study with the inclusion of learning and action selection may also help to model the reinforcement learning capability of the BG network. [ABSTRACT FROM AUTHOR]

In this research, after data preparation, disease prediction has been attempted using large matrix methods and data mining techniques. By examining the new vector, we can find out which of the diseases in the matrix will be closer to this new disease with new symptoms using the rows of the matrix. The conducted research is one of descriptive-analytical and applied studies. Data mining analyzes databases and large datasets to discover and extract valuable information. Such studies and explorations can be considered as an extension and continuity of ancient knowledge and intertwined statistics. The major difference lies in the scale, scope, and diversity of fields and applications, as well as the dimensions and sizes of today's data, where machine learning methods related to learning, modeling, and training are used in computer science to discover patterns among data, usually raw and often meaningless data enters the system and after necessary processing, results are extracted from the data, which are called information. General applications of data mining in computer science include: discovering patterns among data; approximate prediction of results. In this research, after data preparation, disease prediction has been attempted using large matrix methods and data mining techniques. By examining the new vector, we can find out which of the diseases in the matrix will be closer to this new disease with new symptoms using the rows of the matrix. The conducted research is one of descriptive-analytical and applied studies. [ABSTRACT FROM AUTHOR]

Health concerns regarding the presence of cyanobacteria cells and related metabolites (cyanotoxins, taste and odorous substances) in water resources have raised in the last decade. The number of cyanobacteria in water resources is higher than 10000 cells/ml in some of the cases. This values of cyanobacteria also increase the demands on water treatment technology. At the same time, exceeding the value of 20000 cells/ml in water resource is a reason for monitoring the quality of drinking water and the value of Microcystin LR in treated drinking water. Diseases caused by cyanobacteria and cyanotoxins include disorders of the digestive tract, allergic reactions, respiratory problems, contact dermatitis, liver and kidney diseases, weakening of the immune system, skin and liver cancer, nervous system diseases. The intensive physical and chemical treatment is needed, e.g. coagulation, flocculation, sedimentation, filtration, adsorption (activated carbon), disinfection (ozone, chlorination), eventually the combination of mechanical, physio-chemical and biological treatment methods with the disinfection. Removal cyanobacteria require the further options to be sought to improve the treatment technology efficiency of drinking water. Ultrafiltration (without coagulation) was investigated within the pilot-plant tests at the WTP Turček during the treatment of surface water originating from water reservoir Turček. Water treatment was focused on cyanobacteria, turbidity, colour and humic substances (expressed by the COD - chemical oxygen demand parameter) removal from water. Fully automated ultrafiltration equipment with the membrane module UA-640 (Microdyn-Nadir) was used. The ultrafiltration unit treated the water for 3-4 hours, filtration took place for 30 minutes, then washing with treated water followed for 15 seconds and the entire filtration cycle was repeated several times. On the base of filtration cycles, the effectiveness of membraned technology was evaluated. By the application of membrane technology used, the required quality of treated water has been achieved. The results show 100% cyanobacteria removal efficiency, 38.8% CODMn reduction efficiency, 78.6% turbidity removal efficiency, water colour was reduced by 80.8%. The turbidity value in the treated water ranged from 0.32 to 0.42 NTU. Ultrafiltration membrane processes generally remove viruses and bacteria with an efficiency of 99.99%, There are no published data on the removal of filamentous cyanobacteria in the literature. Based on the results achieved, it clearly follows that this method of treatment is suitable for this type of water and achieving the stricter limits specified in the new Directive of the European Parliament and the Council (EU) 2020/2184 for drinking water. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an intricate neurological disorder in which degeneration of brain cells occurs. A recent study has demonstrated that around thirty-six million people are affected with AD. It has been found that the number of deaths due to Alzheimer's disease increased 146.2% between 2000 and 2018. The various evidence collected till date, indicate that β-amyloid is pivotal to the pathogenesis of AD and hence is main factor responsible for AD. The BACE1 enzyme is essential for the generation of β-amyloid and it has been found that level of BACE1 increases in persons with AD. The abnormal proteins distort the neurons. Moreover, as brain cells become affected, the level of neurotransmitters (chemical messengers), between the brain cells which are involved in transmitting signals in the brain decreases. Hence, researcher's need to consider various factors which cause Alzheimer's disease so that a well-defined approach can be adopted for focused drug discovery and development. As AD is a highly complex neurological disorder, therefore the researchers need to adopt a synthetic approach which is "Multi-Target-Directed Ligand". The present review is based on the major advancements in the synthesis of compounds containing 1,2,3-triazole pharmacophore as cholinesterase (AChE and BuChE) inhibitors as well as (BACE1) inhibitors for the period 2007 to 2020. [ABSTRACT FROM AUTHOR]

Objective: There are many neurodegenerative disorders caused by different mechanisms. Mainly these diseases occur due to loss of neuronal function. In the world about 30 million people are suffering from neurological diseases and these diseases include Parkinsonism, depression, Alzheimer's disease, epilepsy many others. In this paper, we are focusing on Parkinsonism. The goal of this research is to forecast the anti-Parkinson effect of phytoconstituents from Curcuma albiflora and Curcuma aurantica utilizing the prediction of activity spectra of substance (PASS). Method: In the study, we have used the PASS software which helped to predict the potential effect of a different chemical constituent of Curcuma albiflora and Curcuma aurantiaca. The prediction is done by using a canonical simplified molecular input line entry system which is taken from the PubChem website and compared with the marked drug levodopa. Result: From this study, we obtained that 1,8- cineole and α- pinene have all activities from the selected target but β elemene, β elemenone, caryophyllene have the least activities for the selected target sites. Conclusion: We tried to collect the beneficial effect of Curcuma albiflora and Curcuma aurantiaca from PASS regarding anti – Parkinson activity. We concluded that 1,8- cineole and α- pinene are beneficial for future research. [ABSTRACT FROM AUTHOR]

Toxoplasmosis is the common name for disease and infection in human and animal caused by the opportunistic parasite named Toxoplasma gondii. That parasite is which is obligated intracellular parasite that capable of infected all warm–blood animals including mammals and birds, toxoplasmic encephalitis and congenital birth deficiencies caused by this parasite. Toxoplasmosis can cause serious pathological conditions such as hepatitis, blindness, abortion, pneumonia, and cruel neurological disorders. These conditions are most common in patients who have weakened immune systems. According to reports that T. gondii can alter personality traits in both men and women, it's likely that alterations in sexual hormones play a part in Toxoplasma infection. Androgen such as testosterone which is circulating hormone in blood stream, its production occur in Leyding cells in testicles. Testosterone hormone play essential role in development of male characteristic, it promotes the development of secondary sexual characteristic in men, such as facial hair growth in axillae, the larynx cartilage growth, prostate growth, and development in men and depth of men sound [ABSTRACT FROM AUTHOR]

Alzheimer's infection (AD) is a neurodegenerative illness and the most widely recognized reason for Dementia in more seasoned grown-ups. The piece of cerebrum that gets influenced by this illness is hippocampus degeneration. The Discovery of Alzheimer's sickness at the fundamental stage is vital as it can forestall genuine harm to the patient's mind. It becomes hazardous and here and there lethal in the event of individuals 65 years old or above. To fulfil the expectation, CNN calculations were used. The suggested technique has obtained a 20 percent improvement in characterisation accuracy, which is contrasted with conventional procedures, and suggests that neural organism is a tremendous advantage for neurological disease analysis. Due to our study, the analysis of different neurological diseases using intelligent medical services frameworks may be done by comparable or comparative ways.The principal objective of this paper is to utilize Hybrid AI calculations that coordinate SVM with CNN and component extraction and determination to anticipate Alzheimer's sickness and fabricate a helpful model. The dataset is taken as MRI pictures. The proposed approach identifies the different phases of Alzheimer's infection, for example, moderate-unbalanced and no sick utilizing CNN calculation. [ABSTRACT FROM AUTHOR]

Alzheimer's can permanently destroy brain cells that are involved in memory and thinking ability. Alzheimer's is a neurological disease that has a major influence on sufferers' life. Early identification and treatment of Alzheimer's patients is critical since it can help postpone the disease's progression and symptoms. This study designed a classification system for Alzheimer's disease into four classes; Very Mild Demented, Mild Demented, Moderate Demented and Non Demented. The system designed using CNN (Convolutional Neural Network) method with Efficient-Net architecture based on MRI (Magnetic Resonance Imaging). The input is taken from the Kaggle Alzheimer's Dataset with a total of 1600 images. The optimization is performed using Adam, Adamax, Nadam, RMSprop, and SGD. The best system performance has been carried out with Nadam optimizer with highest accuracy value of 0.97, precision value of 0.97, recall value of 0.97, f1-score of 0.97 and loss of 0.1104. Based on the performance results, the system shows that the CNN model with Efficient-Net architecture can classify the conditions of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Huntington's disease (HD) is an inherited neurodegenerative disease, causing a hyperkinetic movement disorder with uncontrollable movements. The incidence of HD in western countries is about 0.006% and shows a close relationship with the family inheritance. HD will cause great distress to the patient's body and mind, and the life expectancy of patients is at about 15-20 years. Although the researcher has been extensive, there is no suitable solution found to completely cure this neurological disorder. This work will explain the pathogenesis of Huntington's disease and examine the relationship between the high oxidative stress and the inability of sp1 to transcript the CSE gene. The experiment validates the effect of the Sp1 cluster on the content of cysteine, which may be part of the cell damage mechanism in HD. [ABSTRACT FROM AUTHOR]

BDNF is an important regulator of synaptic circuit development, synaptic plasticity and neuroprotection in the brain. Changes in the concentration of BDNF in the human body have been linked to neurodegenerative diseases, neurological diseases, psychological diseases and so on. In order to better understand BDNF concentration changes in vivo, it is important to understand BDNF expression and release. Most studies on BDNF expression and release have been conducted during the preparation and culture of neurons. Therefore, this paper will focus on the expression and release of BDNF in different cells in the brain. In this paper, the synthesis, processing, sorting and induced release of BDNF will be reviewed. [ABSTRACT FROM AUTHOR]

Autism is a neurological disorder, in which affected person might be unable to talk, express, socialize. There could be many more other incapabilities with varying severities, so it is also known as autistic spectrum disorder (ASD). Autism could not be cured in general and procrastination in ASD detection could make the situation deteriorated. But situation could be make better if it is diagnosed and early intervened by appropriate therapies. The detection of autism in toddlers is done by trained medical practitioners but there are lack of trained medical practitioners available for autism detection in many countries specially developing countries. In this paper we presented a comparative study for autism detection in toddlers through various machine learning techniques. In the study we compare precesion-recall curve and accuracies of different machine learning models. We used a publicly available dataset "Autistic Spectrum Disorder Screening Data for Toddlers" for our study. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a crippling mental problem portrayed by daydreams and hear-able fantasies, just as weaknesses in memory, consideration, chief, and numerous other high-request psychological capacities. The advancement of attractive reverberation imaging (MRI) has offered a powerful and non-invasive way to deal with analyzing the mind's life systems and has incited various researchers to investigate the hidden neuropathology of SZ. By far, most studies show that dark matter (GM) decreases in the cerebrums of SZ patients ordinarily include the worldly, front-facing, and parietal lobes.GM irregularities in the occipital projection, insula, striatum, thalamus, cerebellum and cingulated locales have additionally been accounted for. The picture arrangement procedure is the way toward further developing the analysis interaction. It partitions and marks the pictures into numerous predefined classes. There are different strategies accessible, such as SVM, Random backwoods, Boltzmann, etc. This paper gives a model where DNN strategy with dim scaled division method. With these two procedures joined, it provides a superior outcome with lesser computational time. [ABSTRACT FROM AUTHOR]

Epilepsy, one of the most common neurological conditions characterized by epileptic seizures, is the second most common neurological disorder behind stroke, according to the World Health Organization (WHO). Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. Patients with epilepsy suffer from sudden and unforeseen seizures, during which they are unable to protect themselves and are vulnerable to suffocation, death, or injury due to fainting and traffic accidents. Electroencephalography (EEG) plays an important role in detecting epilepsy, as it measures differences in voltage changes between electrodes along the subject's scalp by sense ionic currents flowing within brain neurons and provides temporal and spatial information about the brain. Electroencephalography (EEG) is an effective and non-invasive technique for directly recording the brain electrical activity and diagnosing clinical brain diseases. Detection with EEG requires a direct examination by a physician as well as a substantial amount of time and effort. The development of an automated, computer-aided method for the diagnosis of epilepsy is urgently needed. Electroencephalography (EEG) signals play important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, analyzing all EEG recordings to detect epileptic activity is quintessential. [ABSTRACT FROM AUTHOR]

Environmental and health issues have risen as living conditions and economic growth have improved. The haze weather phenomenon is becoming a serious problem as air quality continues to deteriorate. It has been discovered that there is a possible connection or causal relationship between exposure to air pollution and neurological diseases. The haze weather phenomenon is integrated into fog as an extreme weather warning in many geographical areas. The design and implementation of an embedded multisensory system for air pollution and air quality monitoring with disease prevention warnings and alarms is presented in this paper. This device is made up of a microcontroller and various sensors formeasuring gases in the atmosphere. This system also includes a model to predict diseases in a more efficient way other than the present conventional methods. More-over an immediate alert system is also included with display and enables data collection of these sensors for future machine learning and artificial intelligence systems. [ABSTRACT FROM AUTHOR]

The use of serious games is more and more popular in the medical filed, mainly for home-based rehabilitation therapy. In this context, some design principles were already proposed. Designing serious game for evolution diseases assessment is challenging and necessitates a new approach of this principles. Working with physiotherapists at the origin of the Motor Function Measurement assessment scale, the development of a dedicated serious game is proposed based on new contextual principles. [ABSTRACT FROM AUTHOR]

Based on the analysis of the Russian functional nutrition market, it can be concluded that the diversity of functional beverages for preventive purposes is small. In this connection, the formulation of a functional beverage of adaptogenic action based on plant materials was developed. The beverage is aimed at reducing the risk of diseases of the brain and nervous system. Since filtration is an integral part of any technological process to produce functional beverages, this process was excluded, and the effect of filtration on the content of mineral elements in the beverage was studied. After analysing the various filtration methods, the microfiltration method was selected as the most suitable to achieve brilliance and microbiological stability. The mineral composition was determined by atomic absorption spectrophotometry, after which the values before and after filtration were compared. As a result of the experiment, changes in the concentrations of mineral elements were proved: potassium remained unchanged, copper and iron decreased by 25.4% and 79.0%, respectively, manganese, calcium, sodium, magnesium decreased slightly (within the error). [ABSTRACT FROM AUTHOR]

It is fairly obvious that we live in a toxic world and our brains are poisoned continuously, and this constant low level of exposure over time is slowly damaging our neurons and brain cells. The worse news is that this harmful toxic environment is not going to stop anytime in the foreseeable future and environmental toxins are a leading cause of neurological illnesses. It is also well established that oxidative stress contributes to various diseases including diabetes, heart disease, inflammatory conditions, high blood pressure, and neurological diseases such as Alzheimer's and Parkinson's. It is therefore inevitable that exposure to such conditions including heavy metals, EMF radiation, bacteria and viruses will require a strong immune system to defeat such harmful toxins, and that almost 80 percent of the immune system is located in the gut. The main causes of neurological diseases are already known and the standard drug treatments (e.g., Aricept and Namenda for Alzheimer's) slow the progression of such terrible diseases, but they do not address the root cause of memory loss. Glutathione is known as body's master antioxidant being effective against oxidative stress and inflammation. Along with other natural antioxidants in the body, it works to defeat the harmful effects of free radicals which cause damage to cells and tissue leading to various health conditions. In this review, the main factors, including environmental toxins, causing neurological illnesses like Alzheimer's and Parkinson's, and natural ways of prevention and treatment protocols have been discussed. The proposed treatment protocol could be lifesaving and is supported by the extensively published literature. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a chronic neuropsychiatric disease that affects the central nervous system, the main symptoms of which are tremors, the difficulty of movement and balance problems, as well as non- motor symptoms such as anxiety and depression. It is believed to be a multi-cause disease. Latent toxoplasmosis occurs when cystic or bradyzoite phases of the parasite Toxoplasma gondii reside into the brain cells and other tissues in host body, where the parasite begins to manipulate the behavior of the host and can cause neurological and psychological symptoms in some infected individuals. Therefore, this parasite is a dangerous source of many neurological diseases such as schizophrenia and dementia. A total of 80 samples were collected, 35 samples for individuals with Parkinson's (19 males and 16 females) and 45 samples for healthy control (33 males and 12 females). IgG antibodies, which had been detected against toxoplasma parasite indicate presence of chronic or latent infection. This had been done by using the ELISA test during the period from November 2017 to March 2018. The collected samples of the control group included volunteer donors from Najaf province, while samples with Parkinson's disease were collected from various provinces located in the middle of Iraq (Najaf, Babylon and Baghdad). The results showed that the percentage of chronic infection (Positive IgG antibodies) was significantly higher (P <0.05) in patients with Parkinson's (65.71%) than in healthy control (37.77%) by using Chi square test (X2). There no significant differences in the percentage of infection between males and females in all study groups. In conclusion, the current study find that there is may be a close relationship between chronic toxoplasmosis and Parkinson's disease. [ABSTRACT FROM AUTHOR]

Nerve Diseases are one of the most important health issues faced by a majority of the population of the world. They can range from as much as a small tooth sensitivity to more complex nervous diseases like Parkinson's disease or Parkinsonism. It is essential to have a frame work that can effectually recognize the prevalence of Parkinsonism in thousands of samples instantaneously. In this paper the potential of nine classification techniques is evaluated for prediction of Parkinsonism. Namely decision tree, naive Bayesian neural network, SVM, ANN, KNN. The proposed algorithm of SVM (support vector machine) employs in Parkinsonism prediction. Using medical profiles such as age, sex, blood pressure, muscle electric activity, EMG Evaluation, it can predict likeliness of patients getting Parkinsonism. Based on this, medical society takes interest in detecting and preventing the nerve disease. From the analysis, it has been proved that classification based techniques contribute high effectiveness and obtain high accuracy when compared to others. [ABSTRACT FROM AUTHOR]

L-type calcium channel (LTCC) gene Cav1.2 on astrocytes play an essential role in the alteration of Ca2+ homeostasis and a line of calcium-related glial-cell functions and neuro-glial dialogue. A wealth of experiment revealed that oxidative stress and reactive astrogliosis account for most neurological diseases. Existing studies shows oxidative stress induces influx of Ca2+ into the cell through LTCC. In this study, we directly investigated the effect of oxidative stress inducing by hydrogen peroxide (H2O2) on LTCC Cav1.2 α1-subunit. We found that H2O2 upregulated LTCC Cav1.2 α1-subunit protein but not mRNA, suggesting H2O2-induced changes in Cav1.2 α1-subunits most likely through posttranscriptional mechanism. [ABSTRACT FROM AUTHOR]

Amyotrophic Lateral Sclerosis (ALS) is a severe neurological disease; as a result, patient care requires a complex network of specialists and equipment providers. Despite best intentions, the organizational and regulatory structures in Germany have not been geared towards efficient and effective coordination of care for patients and their caregivers. This situation calls for innovation of care procurement and provision. APST is a multi-sided, patient-centric platform, addressing the coordination of ALS care by combining case management and platform-based service/equipment exchanges. Over time two feedback cycles, quality of platform service and of care, as well as two research cycles, evidencebased care and medical research, have been established and the platform has become an infrastructure for ALS care, exemplifying social entrepreneurship for digital innovation in patient care. We use an interorganizational configuration lens (Lyytinen and Damsgaard 2011) to reconstruct how the organizing logic shaped four components of APST (case management, transaction, information, and research platform). [ABSTRACT FROM AUTHOR]

Cerebral Palsy is a neurological disease. People with this disease have difficulty in doing daily movements such as walking, speaking, and comprehension. This neurological disease is tried to be treated with rehabilitation techniques. In this study, an exercise game is designed for cerebral palsy patients. The aim is to develop a system in which children with cerebral palsy can perform daily exercises that they need amused and the treatment can be followed. The kinect sensor is used to detect the position in the study, the Unity 3D game engine is used to feedback the accuracy of the movement. In the designed system, the avatar and the patient's exercise are matched. The game avatar verbally informs the patient whether he/she is doing it correctly or not. Scoring is done according to whether the move is made right or wrong. Thus, exercises that are mandatory for pediatric patients and other age groups are turned into a fun activity regardless of location and time. With the development of such a system, the crowd in physical therapy centers will decrease, and patients will be able to continue their treatment under all conditions, such as pandemics. [ABSTRACT FROM AUTHOR]

C. elegans, better known as a roundworm nematode, is a popular model organism used in studying neurological disorders like dementia. Our research focuses on a specific protein, APL-1, which is an ortholog similar to human amyloid precursor protein that is linked to early onset Alzheimer's disease. The ynIs-79 nematode strain is an overexpression mutant in which accumulation of the protein causes larval lethality, molting defects, and motor dysfunction. This mutant overexpresses APL-1 in ventral cord neurons and is tagged with a GFP marker. Thus far, we are using traditional, ultrasonic bath, and homogenization protein extraction techniques to determine the baseline concentrations of total protein in various C. elegans strains. These techniques will serve to maximize total protein concentration to quantify APL-1 using Western Blot analysis. The traditional protein extraction yields low total protein and reduces the chances of identifying APL-1, while the ultrasonic bath extraction technique yields higher total protein. In summary, optimizing protein isolation will allow us to accurately quantify the APL-1 protein at specific stages of the nematode's life cycle when raised in the presence of various antioxidant compounds. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) belongs to the Flaviviridae family and its recent outbreak and association with human diseases (e.g. neurological disorders and microcephaly in babies) have raised global health concerns. Its differentiation from Dengue in accurate diagnostic assays are of great importance. The viral NS1 protein is a secreted protein and can be an ideal target for diagnostic assay development. We have thus selected NS1 as target for aptamer selection and study. Single-stranded DNA oligonucleotides aptamers have been previously selected for this target, and here we present the study of such aptamers by fluorescent spectroscopy, CD and ELISA in order to characterize their interaction and select the best candidate for further future assay development. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) belongs to the Flaviviridae family and its recent outbreak and association with human diseases (e.g. neurological disorders) have raised global health concerns. The viral NS5 protein is the most conserved ZIKV protein, and is comprised of two domains, an N-terminal methyltransferase domain and an RNA-dependent RNA polymerase (RdRP) domain at the C-terminal end. The fact that viral NS5 is essential for viral replication but lacks a human counterpart makes NS5 an important target to be studied. Single-stranded ONA oligonucleotides aptamers can fold into unique three-dimensional conformations that recognize targets with high-specific and -affinity. This work aims to study single-stranded ONA aptamers displaying high affinity and specificity generated against ZIKV NS5 protein to be used in diagnostic platforms. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is often related to motor symptoms, this neurological disorder involves multiple and complex factors including age, genetics, and dopamine production. These factors leave a trail in substantia nigra (SN), observed as abnormal iron aggregates in ventral midbrain area sections, obtained from adult male Wistar rats. Understanding the iron accumulation process increases the knowledge underlying the neurodegenerative causes. This is achieved by using modern computational tools to directly analyze experimental data. Merging these disciplines creates new areas of study and innovation. [ABSTRACT FROM AUTHOR]

According to statistics, as of the beginning of 2019, traumatic brain injuries (TBI) are considered to be the most frequent type of injury in Russia. About 600 thousand cases are annually registered in the country. This makes it the second frequent complaint after a headache caused by a brain injury. The clinical picture of neurological disorders associated with dizziness caused by a brain injury of any severity depends on the nature of the damage to the vestibular centers. Positional and pneumatic tests performed in cases of suspected traumatic brain injury can detect a labyrinth lesion, and this affects the final diagnosis and patient care methods on an ambulatory basis. [ABSTRACT FROM AUTHOR]

Chronic pain caused by a disease or lesion of the nervous systems. Current treatments can provide a long- lasting pain relief only in a very limited number of patients with chronic pain. MicroRNA can regulate multiple genes and pathways involved in human diseases. This review focuses on: Aberrant expression of microRNAs and their potential therapeutic targets in chronic pain and MicroRNA therapy in clinical practice.Studies from rodent models of neuropathic pain and patients have a role for miRNAs in mediating various aspects of pain processing.MicroRNA can modulated Neuropathic pain pathways and could serve as clinical therapeutic targets. [ABSTRACT FROM AUTHOR]

To help in understanding the physical and biological mechanisms underlying effects of cosmic and therapeutic types of radiation on the central nervous system (CNS), we have developed an original neuron application based on the Geant4 Monte Carlo simulation toolkit, in particular on its biophysical extension Geant4-DNA. The applied simulation technique provides a tool for the simulation of physical, physico-chemical and chemical processes (e.g. production of water radiolysis species in the vicinity of neurons) in realistic geometrical model of neural cells exposed to ionizing radiation. The present study evaluates the microscopic energy depositions and water radiolysis species yields within a detailed structure of a selected neuron taking into account its soma, dendrites, axon and spines following irradiation with carbon and iron ions. [ABSTRACT FROM AUTHOR]

The present study is focused on the development of optimal models of neuron morphology for Monte Carlo microdosimetry simulations of initial radiation-induced events of heavy charged particles in the specific types of cells of the hippocampus, which is the most radiation-sensitive structure of the central nervous system. The neuron geometry and particles track structures were simulated by the Geant4/Geant4-DNA Monte Carlo toolkits. The calculations were made for beams of protons and heavy ions with different energies and doses corresponding to real fluxes of galactic cosmic rays. A simple compartmental model and a complex model with realistic morphology extracted from experimental data were constructed and compared. We estimated the distribution of the energy deposition events and the production of reactive chemical species within the developed models of CA3/CA1 pyramidal neurons and DG granule cells of the rat hippocampus under exposure to different particles with the same dose. Similar distributions of the energy deposition events and concentration of some oxidative radical species were obtained in both the simplified and realistic neuron models. [ABSTRACT FROM AUTHOR]

The article discusses the underlying premises for the ethical standpoint of Actor-Network-Theory (ANT) and explore other existing alternatives. It presents information on the human-technology relations and Actor-Network-Theory. It presents information on transformations of media technologies and how they change the way humans think and act, environmental and mental changes she claims have had significant neurological disorders.

The article offers insight to a study conducted for examining the effects of video gaming on activity within the hippocampus to promote memory. It informs that the gaming platform has been linked to enhanced language learning, visual perception learning, and declarative memory. It also informs that video games provide a potentially useful platform for building effective learning tools for neurological disorders that impact memory.

Our research explores the potential of wearable Mixed Reality (MR) for people with Neuro-Developmental Disorders (NDD). The paper presents HoloLearn, a MR application designed in cooperation with NDD experts and implemented using HoloLens technology. The goal of HoloLearn is to help people with NDD learn how to perform simple everyday tasks in domestic environments and improve autonomy. An original feature of the system is the presence of a virtual assistant devoted to capture the user's attention and to give her/him hints during task execution in the MR environment. We performed an exploratory study involving 20 subjects with NDD to investigate the acceptability and usability of HoloLearn and its potential as a therapeutic tool. HoloLearn was well-accepted by the participants and the activities in the MR space were perceived as enjoyable, despite some usability problems associated to HoloLens interaction mechanism. More extensive and long term empirical research is needed to validate these early results, but our study suggests that HoloLearn could be adopted as a complement to more traditional interventions. Our work, and the lessons we learned, may help designers and developers of future MR applications devoted to people with NDD and to other people with similar needs. [ABSTRACT FROM AUTHOR]