Search results for: deep brain stimulation

Authors: Yung-Chih Kuo, I-Hsuan Lee

Abstract:

Solid lipid nanoparticles (SLNs) conjugated with aprotinin (Apr) and melanotransferrin antibody (Anti-MTf) were used to carry doxorubicin (Dox) across the blood–brain barrier (BBB) for glioblastoma multiforme (GBM) chemotherapy. Dox-entrapped SLNs with grafted Apr and Anti-MTf (Apr-Anti-MTf-Dox-SLNs) were applied to a cultured monolayer comprising human brain-microvascular endothelial cells (HBMECs) with regulation of human astrocyte (HAs) and to a proliferated colony of U87MG cells. Based on the average particle diameter, zeta potential, entrapping efficiency of Dox, and grafting efficiency of Apr and Anti-MTf, we found that 40% (w/w) 1,2-dipalmitoyl-sn-glycero-3-phosphocholine in lipids were appropriate for fabricating Apr-Anti-MTf-Dox-SLNs. In addition, Apr-Anti-MTf-Dox-SLNs could prevent Dox from fast dissolution and did not induce a serious cytotoxicity to HBMECs and HAs when compared with free Dox. Moreover, the treatments with Apr-Anti-MTf-Dox-SLNs enhanced the ability of Dox to infuse the BBB and to inhibit the growth of GBM. The current Apr-Anti-MTf-Dox-SLNs can be a promising pharmacotherapeutic preparation to penetrate the BBB for malignant brain tumor treatment.

Keywords: solid lipid nanoparticle, glioblastoma multiforme, blood–brain barrier, doxorubicin

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Authors: Abeer Ahmed Abdelhameed

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The aim of this study was to investigate the effect of aerobic exercises on LV parameters, lipid profile, and anthropometric measurements in hypertensive middle aged male subjects. Thirty obese patients were recruited for the study from the outpatient clinic of National Heart Institute, Egypt. Their ages ranges from 40 to 60 years. All participants underwent an aerobic training program including regular aerobic sub-maximal exercises in the form of treadmill walking and abdominal exercises 3/week for four months, the exercise were individually tailored for each participant depending on the result of cardiopulmonary exercise test. The result showed no significant difference observed in both LVPWT and LVSWT data from pre-test values to post-test values in all subjects after 4 months, with a significant reduction in WHR, systolic blood pressure, TAG and LDL records. Result also revealed a significant increase in HDL, Eƒ, LVEDD and FS records for all participants. The significant improvement in ventricular functions in form of ejection fraction of electrical group more than exercise group after 4 months at the end of the study may be due to the beneficial effect of faradic stimulation in lipolysis of storage adipose tissues, stimulation of lean body mass and muscles and/or thermal effect that improves vascularization.

Keywords: left ventricular parameters, aerobic training, electrical stimulation, lipid profile

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Ashkan Shafee, Ahmad Fahimifar

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There is a growing demand for construction of high-rise buildings and infrastructures in large cities, which sometimes require deep excavations in the vicinity of pile foundations. In this study, a two-dimensional finite element analysis is used to gain insight into the response of pile groups adjacent to deep excavations in sand. The numerical code was verified by available experimental works, and a parametric study was performed on different working load combinations, excavation depth and supporting system. The results show that the simple two-dimensional plane strain model can accurately simulate the excavation induced changes on adjacent pile groups. It was found that further excavation than pile toe level and also inclined loading on adjacent pile group can severely affect the serviceability of the foundation.

Keywords: deep excavation, inclined loading, lateral deformation, pile group

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Asmaa El Harat, Toumi Hicham, Youssef Baddi

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This survey delves into the complex realm of Internet of Things (IoT) security, highlighting the urgent need for effective cybersecurity measures as IoT devices become increasingly common. It explores a wide array of cyber threats targeting IoT devices and focuses on mitigating these attacks through the combined use of deep learning and machine learning algorithms, as well as edge and cloud computing paradigms. The survey starts with an overview of the IoT landscape and the various types of attacks that IoT devices face. It then reviews key machine learning and deep learning algorithms employed in IoT cybersecurity, providing a detailed comparison to assist in selecting the most suitable algorithms. Finally, the survey provides valuable insights for cybersecurity professionals and researchers aiming to enhance security in the intricate world of IoT.

Keywords: internet of things (IoT), cybersecurity, machine learning, deep learning

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Authors: Fumihiro Ima, Shinichi Watanabe, Shingo Maeda, Haruna Imai, Hiroki Niimi

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It is important to know growth rate of brain tumors before surgery because it influences treatment planning including not only surgical resection strategy but also adjuvant therapy after surgery. Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging (MRI) technique based on chemical exchange saturation transfer without administration of contrast medium. The underlying assumption in APT imaging of tumors is that there is a close relationship between the proliferative activity of the tumor and mobile protein synthesis. We aimed to evaluate the diagnostic performance of APT imaging of pre-and post-treatment brain tumors. Ten patients with brain tumor underwent conventional and APT-weighted sequences on a 3.0 Tesla MRI before clinical intervention. The maximum and the minimum APT-weighted signals (APTWmax and APTWmin) in each solid tumor region were obtained and compared before and after clinical intervention. All surgical specimens were examined for histopathological diagnosis. Eight of ten patients underwent adjuvant therapy after surgery. Histopathological diagnosis was glioma in 7 patients (WHO grade 2 in 2 patients, WHO grade 3 in 3 patients and WHO grade 4 in 2 patients), meningioma WHO grade1 in 2 patients and primary lymphoma of the brain in 1 patient. High-grade gliomas showed significantly higher APTW-signals than that in low-grade gliomas. APTWmax in one huge parasagittal meningioma infiltrating into the skull bone was higher than that in glioma WHO grade 4. On the other hand, APTWmax in another convexity meningioma was the same as that in glioma WHO grade 3. Diagnosis of primary lymphoma of the brain was possible with APT imaging before pathological confirmation. APTW-signals in residual tumors decreased dramatically within one year after adjuvant therapy in all patients. APT imaging demonstrated excellent diagnostic performance for the planning of surgery and adjuvant therapy of brain tumors.

Keywords: amides, magnetic resonance imaging, brain tumors, cell proliferation

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Authors: Asmaa Tahiri, Mouktafi Amine

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A substantial number of traditional medical research has been put forward to managing and treating mental disorders. At the present time, to our best knowledge, it is believed that fundamental understanding of the underlying causes of the majority psychological disorders needs to be explored further to inform early diagnosis, managing symptoms and treatment. The emerging field of evolutionary psychology is a promising prospect to address the origin of mental disorders, potentially leading to more effective treatments. Schizophrenia as a topical mental disorder has been linked to the evolutionary adaptation of the human brain represented in the brain connectivity and asymmetry directly linked to humans higher brain cognition in contrast to other primates being our direct living representation of the structure and connectivity of our earliest common African ancestors. As proposed in the evolutionary psychology scientific literature the pathophysiology of schizophrenia is expressed and directly linked to altered connectivity between the Hippocampal Formation (HF) and Dorsolateral Prefrontal Cortex (DLPFC). This research paper presents the results of the use of tractography analysis using multiple open access Diffusion Weighted Imaging (DWI) datasets of healthy subjects, schizophrenia-affected subjects and primates to illustrate the relevance of the aforementioned brain regions connectivity and the underlying evolutionary changes in the human brain. Deterministic fiber tracking and streamline analysis were used to generate connectivity matrices from the DWI datasets overlaid to compute distances and highlight disconnectivity patterns in conjunction with other fiber tracking metrics; Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD).

Keywords: tractography, evolutionary psychology, schizophrenia, brain connectivity

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Authors: Karima Merchant

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The aim of this study was to analyze the impact of the Writing Eight Exercise, an exercise from the Brain Integration Therapy, with Kindergartners who are struggling with writing tasks in school. With the help of this exercise, children were able to cross the midline, an invisible line running from our brain to our feet, which separates the body’s right from left. Crossing the midline integrates the brain hemispheres, thus encouraging bilateral movement. The study was spread over 15 weeks where the children were required to do the Writing Eight Exercise 4 times a week. The data collection methods included observations, student work samples and feedback from teachers and parents. Based on the results of this study, it can be concluded that the Writing Eight Exercise had a positive impact on students’ approach towards writing tasks, letter formation, and fine motor skills.

Keywords: crossing the midline, fine motor skills, letter formation, writing

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Authors: Pawan S. Nagda, Purnank S. Bhatt, Mit K. Shah

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Earing defect in drawing process is highly undesirable not only because it adds on an additional trimming operation but also because the uneven material flow demands extra care. The objective of this work is to study the earing problem in the Deep Drawing of circular cup and to optimize the blank shape to reduce the earing. A finite element model is developed for 3-D numerical simulation of cup forming process in ABAQUS. Extra-deep-drawing (EDD) steel sheet has been used for simulation. Properties and tool design parameters were used as input for simulation. Earing was observed in the simulated cup and it was measured at various angles with respect to rolling direction. To reduce the earing defect initial blank shape was modified with the help of anisotropy coefficient. Modified blanks showed notable reduction in earing.

Keywords: anisotropy, deep drawing, earing, finite element simulation

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

Procedia PDF Downloads 177

Authors: Fumihiro Imai, Shinichi Watanabe, Shingo Maeda, Haruna Imai, Hiroki Niimi

Abstract:

It is important to know the growth rate of brain tumors before surgery because it influences treatment planning, including not only surgical resection strategy but also adjuvant therapy after surgery. Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging (MRI) technique based on chemical exchange saturation transfer without the administration of a contrast medium. The underlying assumption in APT imaging of tumors is that there is a close relationship between the proliferative activity of the tumor and mobile protein synthesis. We aimed to evaluate the diagnostic performance of APT imaging of pre-and post-treatment brain tumors. Ten patients with brain tumor underwent conventional and APT-weighted sequences on a 3.0 Tesla MRI before clinical intervention. The maximum and the minimum APT-weighted signals (APTWmax and APTWmin) in each solid tumor region were obtained and compared before and after a clinical intervention. All surgical specimens were examined for histopathological diagnosis. Eight of ten patients underwent adjuvant therapy after surgery. Histopathological diagnosis was glioma in 7 patients (WHO grade 2 in 2 patients, WHO grade 3 in 3 patients, and WHO grade 4 in 2 patients), meningioma WHO grade 1 in 2 patients, and primary lymphoma of the brain in 1 patient. High-grade gliomas showed significantly higher APTW signals than that low-grade gliomas. APTWmax in one huge parasagittal meningioma infiltrating into the skull bone was higher than that in glioma WHO grade 4. On the other hand, APTWmax in another convexity meningioma was the same as that in glioma WHO grade 3. Diagnosis of primary lymphoma of the brain was possible with APT imaging before pathological confirmation. APTW signals in residual tumors decreased dramatically within one year after adjuvant therapy in all patients. APT imaging demonstrated excellent diagnostic performance for the planning of surgery and adjuvant therapy of brain tumors.

Keywords: amides, magnetic resonance imaging, brain tumors, cell proliferation

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Okoro Ikechukwu Ralph, Fuat Kara

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The discoveries of hydrocarbon reserves has clearly drifted offshore, and in deeper waters - areas where the industry still has limited knowledge; and that were hitherto, regarded as being out of reach. This shift presents significant and increased challenges in technology requirements needed to guarantee safety of personnel, environment and equipment; ensure high reliability of installed equipment; and provide high level of confidence in security of investment and company reputation. Nowhere are these challenges more apparent than on subsea well integrity and production performance. The past two decades has witnessed enormous rise in deep and ultra-deep water offshore field developments for the recovery of hydrocarbons. Subsea installed equipment at the seabed has been the technology of choice for these developments. This paper discusses the role of Subsea Control module (SCM) as a vital factor for deep-water well integrity and production performance. A case study for Deep-water well integrity and production performance is analysed.

Keywords: offshore reliability, production performance, subsea control module, well integrity

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Authors: Robert H. Bradley, Robert F. Corwyn

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There is abundant research showing that socio-economic status is implicated in parenting. However, additional factors such as family context, parent personality, parenting history and child behavior also help determine how parents enact the role of caregiver. Each of these factors not only helps determine how a parent will act in a given situation, but each can serve to moderate the influence of the other factors. Personality has long been studied as a factor that influences parental behavior, but it has almost never been considered as a moderator of family contextual factors. For this study, relations between three maternal personality characteristics (agreeableness, extraversion, neuroticism) and four aspects of parenting (harshness, sensitivity, stimulation, learning materials) were examined when children were 6 months, 36 months, and 54 months old and again at 5th grade. Relations between these three aspects of personality and the overall home environment were also examined. A key concern was whether maternal personality characteristics moderated relations between household income and the four aspects of parenting and between household income and the overall home environment. The data for this study were taken from the NICHD Study of Early Child Care and Youth Development (NICHD SECCYD). The total sample consisted of 1364 families living in ten different sites in the United States. However, the samples analyzed included only those with complete data on all four parenting outcomes (i.e., sensitivity, harshness, stimulation, and provision of learning materials), income, maternal education and all three measures of personality (i.e., agreeableness, neuroticism, extraversion) at each age examined. Results from hierarchical regression analysis showed that mothers high in agreeableness were more likely to demonstrate sensitivity and stimulation as well as provide more learning materials to their children but were less likely to manifest harshness. Maternal agreeableness also consistently moderated the effects of low income on parental behavior. Mothers high in extraversion were more likely to provide stimulation and learning materials, with extraversion serving as a moderator of low income on both. By contrast, mothers high in neuroticism were less likely to demonstrate positive aspects of parenting and more likely to manifest negative aspects (e.g., harshness). Neuroticism also served to moderate the influence of low income on parenting, especially for stimulation and learning materials. The most consistent effects of parent personality were on the overall home environment, with significant main and interaction effects observed in 11 of the 12 models tested. These findings suggest that it may behoove professional who work with parents living in adverse circumstances to consider parental personality in helping to better target prevention or intervention efforts aimed at supporting parental efforts to act in ways that benefit children.

Keywords: home environment, household income, learning materials, personality, sensitivity, stimulation

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Dur-E- Shahwar

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Much attention has been given to ‘bridging the gap’ between neuroscience and educational practice. In order to gain a better understanding of the nature of this gap and of possibilities to enable the linking process, we have taken a boundary perspective on these two fields and the brain-based learning approach, focusing on boundary-spanning actors, boundary objects, and boundary work. In 26 semi-structured interviews, neuroscientists and education professionals were asked about their perceptions in regard to the gap between science and practice and the role they play in creating, managing, and disrupting this boundary. Neuroscientists and education professionals often hold conflicting views and expectations of both brain-based learning and of each other. This leads us to argue that there are increased prospects for a neuro-scientifically informed learning practice if science and practice work together as equal stakeholders in developing and implementing neuroscience research.

Keywords: language learning, explore, educational practices, mentalist, practice

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Suriya

Abstract:

Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier, Léo Fréchier, Barthélémy Hermenault

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Deep Venous Thrombosis (DVT) occurs when a thrombus is formed within a deep vein (most often in the legs). This disease can be deadly if a part or the whole thrombus reaches the lung and causes a Pulmonary Embolism (PE). This disorder, often asymptomatic, has multifactorial causes: immobilization, surgery, pregnancy, age, cancers, and genetic variations. Our project aims to relate the thrombus epidemiology (origins, patient predispositions, PE) to its structure using ultrasound images. Ultrasonography and elastography were collected using Toshiba Aplio 500 at Brest Hospital. This manuscript compares two classification approaches: spectral clustering and scattering operator. The former is based on the graph and matrix theories while the latter cascades wavelet convolutions with nonlinear modulus and averaging operators.

Keywords: deep venous thrombosis, ultrasonography, elastography, scattering operator, wavelet, spectral clustering

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Authors: Hanan F. Aly, Fateheya M. Metwally, Hanaa H. Ahmed

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The current study is undertaken to elucidate a possible neuroprotective role of dehydroepiandrosterone (DHEA) against the development of Alzheimer’s disease in experimental rat model. Alzheimer’s disease was produced in young female ovariectomized rats by intraperitoneal administration of AlCl3 (4.2 mg/kg body weight) daily for 12 weeks. Half of these animals also received orally DHEA (250 mg/kg body weight, three times weekly) for 18 weeks. Control groups of animals received either DHAE alone, or no DHEA, or were not ovariectomized. After such treatment the animals were analyzed for oxidative stress biomarkers such as hydrogen peroxide, nitric oxide and malondialdehyde, total antioxidant capacity, reduced glutathione, glutathione peroxidase, glutathione reductase, superoxide dismutase and catalase activities, antiapoptotic marker Bcl-2 and brain derived neurotrophic factor. Also, brain cholinergic markers (acetylcholinesterase and acetylcholine) were determined. The results revealed significant increase in oxidative stress parameters associated with significant decrease in the antioxidant enzyme activities in Al-intoxicated ovariectomized rats. Significant depletion in brain Bcl-2 and brain-derived neurotrophic factor levels were also detected. Moreover, significant elevations in brain acetylcholinesterase activity accompanied with significant reduction in acetylcholine level were recorded. Significant amelioration in all investigated parameters was detected as a result of treatment of Al-intoxicated ovariectomized rats with DHEA. These results were confirmed by histological examination of brain sections. These results clearly indicate a neuroprotective effect of DHEA against Alzheimer’s disease.

Keywords: Alzheimer’s disease, oxidative stress, apoptosis, dehydroepiandrosterone

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Authors: Nirupama Dixit, Anyaa Mittal, Neeru Sood

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Alzheimer’s disease (AD) is a progressive form of dementia, contributing to up to 70% of cases, mostly observed in elderly but is not restricted to old age. The pathophysiology of the disease is characterized by specific pathological changes in brain. The changes (i.e. accumulation of metal ions in brain, formation of extracellular β-amyloid (Aβ) peptide aggregates and tangle of hyper phosphorylated Tau protein inside neurons) damage the neuronal connections irreversibly. The current issues in improvement of life quality of Alzheimer's patient lies in the fact that the diagnosis is made at a late stage of the disease and the medications do not treat the basic causes of Alzheimer's. The targeted delivery of drug through the blood brain barrier (BBB) poses several limitations via traditional approaches for treatment. To overcome these drug delivery limitation, nanoparticles provide a promising solution. This review focuses on current strategies for efficient targeted drug delivery using nanoparticles and improving the quality of therapy provided to the patient. Nanoparticles can be used to encapsulate drug (which is generally hydrophobic) to ensure its passage to brain; they can be conjugated to metal ion chelators to reduce the metal load in neural tissue thus lowering the harmful effects of oxidative damage; can be conjugated with drug and monoclonal antibodies against BBB endogenous receptors. Finally this review covers how the nanoparticles can play a role in diagnosing the disease.

Keywords: Alzheimer's disease, β-amyloid plaques, blood brain barrier, metal chelators, nanoparticles

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Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

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Authors: Su Yin Chiam, Zhipeng Ding, Guang Yang, Danny Jian Hang Tng, Peiyi Song, Geok Ing Ng, Ken-Tye Yong, Qing Xin Zhang

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Brain-machine interfaces (BMI) is a ground rich of exploration opportunities where manipulation of neural activity are used for interconnect with myriad form of external devices. These research and intensive development were evolved into various areas from medical field, gaming and entertainment industry till safety and security field. The technology were extended for neurological disorders therapy such as obsessive compulsive disorder and Parkinson’s disease by introducing current pulses to specific region of the brain. Nonetheless, the work to develop a real-time observing, recording and altering of neural signal brain-machine interfaces system will require a significant amount of effort to overcome the obstacles in improving this system without delay in response. To date, feature size of interface devices and the density of the electrode population remain as a limitation in achieving seamless performance on BMI. Currently, the size of the BMI devices is ranging from 10 to 100 microns in terms of electrodes’ diameters. Henceforth, to accommodate the single cell level precise monitoring, smaller and denser Nano-scaled nanowire electrode arrays are vital in fabrication. In this paper, we would like to showcase the fabrication of high aspect ratio of vertical silicon nanowire electrodes arrays using microelectromechanical system (MEMS) method. Nanofabrication of the nanowire electrodes involves in deep reactive ion etching, thermal oxide thinning, electron-beam lithography patterning, sputtering of metal targets and bottom anti-reflection coating (BARC) etch. Metallization on the nanowire electrode tip is a prominent process to optimize the nanowire electrical conductivity and this step remains a challenge during fabrication. Metal electrodes were lithographically defined and yet these metal contacts outline a size scale that is larger than nanometer-scale building blocks hence further limiting potential advantages. Therefore, we present an integrated contact solution that overcomes this size constraint through self-aligned Nickel silicidation process on the tip of vertical silicon nanowire electrodes. A 4 x 4 array of vertical silicon nanowires electrodes with the diameter of 290nm and height of 3µm has been successfully fabricated.

Keywords: brain-machine interfaces, microelectromechanical systems (MEMS), nanowire, nickel silicide

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Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

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Authors: C. Flynn, Q. Yuan, C. Reinhardt

Abstract:

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder affecting broad areas of the cerebral cortex and hippocampus. More than 95% of AD cases are representative of sporadic AD, where both genetic and environmental risk factors play a role. The main pathological features of AD include the widespread deposition of amyloid-beta and neurofibrillary tau tangles in the brain. The earliest brain pathology related to AD has been defined as hyperphosphorylated soluble tau in the noradrenergic locus coeruleus (LC) neurons, characterized by Braak. However, the cause of this pathology and the ultimate progression of AD is not understood. Increasing research points to a connection between the gut microbiota and the brain, and mounting evidence has shown that there is a bidirectional interaction between the two, known as the gut-brain axis. This axis can allow for bidirectional movement of neuroinflammatory cytokines and pathogenic misfolded proteins, as seen in AD. Prebiotics and probiotics have been shown to have a beneficial effect on gut health and can strengthen the gut-barrier as well as the blood-brain barrier, preventing the spread of these pathogens across the gut-brain axis. Our laboratory has recently established a pretangle tau rat model, in which we selectively express pseudo-phosphorylated human tau (htauE14) in the LC neurons of TH-Cre rats. LC htauE14 produced pathological changes in rats resembling those of the preclinical AD pathology (reduced olfactory discrimination and LC degeneration). In this work, we will investigate the effects of pre/probiotic ingestion on AD behavioral deficits, blood inflammation/cytokines, and various brain markers in our experimental rat model of AD. Rats will be infused with an adeno-associated viral vector containing a human tau gene pseudophosphorylated at 14 sites (common in LC pretangles) into 2-3 month TH-Cre rats. Fecal and blood samples will be taken at pre-surgery, and various post-surgery time points. A collection of behavioral tests will be performed, and immunohistochemistry/western blotting techniques will be used to observe various biomarkers. This work aims to elucidate the relationship between gut health and AD progression by strengthening gut-brain relationship and aims to observe the overall effect on tau formation and tau pathology in AD brains.

Keywords: alzheimer’s disease, aging, gut microbiome, neurodegeneration

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Authors: Prima Khairunisa, Febriana Tri Kusumawati, Endah Luthfiana

Abstract:

Background: Hypertension in elderly, caused by cardiovascular system cannot work normally, because the valves thickened and inelastic blood vessels. It causes vasoconstriction of the blood vessels. Hypertension exercise, increase cardiovascular function and the elasticity of the blood vessels. While slow deep breathing helps the body and mind feel relax. Combination both of them will decrease the blood pressure. Objective: To know the effect of hypertension exercise and slow deep breathing combination to blood pressure in elderly. Method: The study conducted with one group pre-post test experimental design. The samples were 10 elderly both male and female in a Village in Semarang, Central Java, Indonesia. The tool was manual sphygmomanometer to measure blood pressure. Result: Based on paired t-test between hypertension exercise and slow deep breathing with systole blood pressure showed sig (2-tailed) was 0.045, while paired t-test between hypertension exercise hypertension exercise and slow deep breathing with diastole blood pressure showed sig (2-tailed) was 0,343. The changes of systole blood pressure were 127.5 mmHg, and diastole blood pressure was 80 mmHg. Systole blood pressure decreases significantly because the average of systole blood pressure before implementation was 135-160 mmHg. While diastole blood pressure was not decreased significantly. It was influenced by the average of diastole blood pressure before implementation of hypertension exercise was not too high. It was between 80- 90 mmHg. Conclusion: There was an effect of hypertension exercise and slow deep breathing combination to the blood pressure in elderly after 6 times implementations.

Keywords: hypertension exercise, slow deep breathing, elderly, blood pressure

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Authors: Hooshmand Kalayeh

Abstract:

The human brain consists of reptilian, mammalian, and thinking brain. And mind consists of conscious, subconscious, and unconscious parallel neural-net programs. The primary objective of this paper is to propose a methodology for quantitative analysis of neural-nets associated with these mental activities in the neocortex. The secondary objective of this paper is to suggest a methodology for quantitative analysis of presence; the proposed methodologies can be used as a first-step to measure, monitor, and understand consciousness and presence. This methodology is based on Neural-Networks (NN), number of neuron in each NN associated with consciousness, subconsciouness, and unconsciousness, and number of neurons in neocortex. It is assumed that the number of neurons in each NN is correlated with the associated area and volume. Therefore, online and offline visualization techniques can be used to identify these neural-networks, and online and offline measurement methods can be used to measure areas and volumes associated with these NNs. So, instead of the number of neurons in each NN, the associated area or volume also can be used in the proposed methodology. This quantitative analysis and associated online and offline measurements and visualizations of different Neural-Networks enable us to rewire the connections in our brain for a more balanced living.

Keywords: brain, mind, consciousness, presence, sub-consciousness, unconsciousness, skills, concentrations, attention

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Authors: Wei-Wen Hu, Yong-Zhi Zhong

Abstract:

Depositing conductive polypyrrole (PPy) onto elastic polydimethylsiloxane (PDMS) substrate can obtain a highly stretchable conductive film, which can be used to construct a bioreactor to cyclically stretch and electrically stimulate surface cells. However, how to completely harvest these stimulated muscle tissue to repair damaged muscle is a challenge. To address this concern, N-isopropylacrylamide (NIPAAm), a monomer of temperature-sensitive polymer, was added during the polymerization of pyrrole on PDMS so that the resulting P(Py-co-NIPAAm)/PDMS should own both conductivity and thermo-sensitivity. Therefore, cells after stimulation can be completely harvested as cell sheets by reducing temperature. Mouse skeletal myoblast, C2C12 cells, were applied to examine our hypothesis. In electrical stimulation, C2C12 cells on P(Py-co-NIPAAm)/PDMS demonstrated the best myo-differentiation under the electric field of 1 V/cm. Regarding cyclic stretching, the strain equal to or higher than 9% can highly align C2C12 perpendicular to the stretching direction. The Western blotting experiments demonstrated that the cell sheets harvested by cooling reserved more extracellular matrix (ECM) than cells collected by the traditional trypsin digestion method. Immunostaining of myosin heavy chain protein (MHC) indicated that both mechanical and electrical stimuli effectively increased the number of myotubes and the differentiation ratio, and the myotubes can be aligned by cyclic stretching. Stimulated cell sheets can be harvested by cooling, and the alignment of myotubes was still maintained. These results suggested that the deposition of P(Py-co-NIPAAm) on PDMS can be applied to harvest intact cell sheets after cyclic stretching and electrical stimulation, which increased the feasibility of bioreactor for the application of tissue engineering and regenerative medicine.

Keywords: bioreactor, cell sheet, conductive polymer, cyclic stretching, electrical stimulation, muscle tissue engineering, myogenesis, thermosensitive hydrophobicity

Procedia PDF Downloads 90

Authors: Sait Ozsoy, Asude Gokmen, Mehtap Yondem, Hanife A. Alkan, Gulnaz T. Javan

Abstract:

Deep neck infection often develops due to upper respiratory tract and odontogenic infections. Gastrointestinal System perforation can occur for many reasons and is in need of the early diagnosis and prompt surgical treatment. In both cases late or incorrect diagnosis may lead to increase morbidity and high mortality. A patient with a diagnosis of deep neck abscess died while under treatment due to sepsis and multiple organ failure. Autopsy finding showed duodenal ulcer and this is reported in the literature.

Keywords: peptic ulcer perforation, peritonitis, retropharyngeal abscess, sepsis

Procedia PDF Downloads 491