Search results for: brain tumor classification

Authors: Karen Boaz, C. R. Charan

Abstract:

Background: The infiltration of tumour stroma by eosinophils, Tumor-Associated Tissue Eosinophilia (TATE), is known to modulate the progression of Oral Squamous Cell Carcinoma (OSCC). Eosinophils have direct tumoricidal activity by release of cytotoxic proteins and indirectly they enhance permeability into tumor cells enabling penetration of tumoricidal cytokines. Also, eosinophils may promote tumor angiogenesis by production of several angiogenic factors. Identification of eosinophils in the inflammatory stroma has been proven to be an important prognosticator in cancers of mouth, oesophagus, larynx, pharynx, breast, lung, and intestine. Therefore, the study aimed to correlate TATE with clinical and histopathological variables, and blood eosinophil count to assess the role of TATE as a prognosticator in Oral Squamous Cell Carcinoma (OSCC). Methods: Seventy two biopsy-proven cases of OSCC formed the study cohort. Blood eosinophil counts and TNM stage were obtained from the medical records. Tissue sections (5µm thick) were stained with Haematoxylin and Eosin. The eosinophils were quantified at invasive tumour front (ITF) in 10HPF (40x magnification) with an ocular grid. Bryne’s grading of ITF was also performed. A subset of thirty cases was also assessed for association of TATE with recurrence, involvement of lymph nodes and surgical margins. Results: 1) No statistically significant correlation was found between TATE and TNM stage, blood eosinophil counts and most parameters of Bryne’s grading system. 2) Statistically significant relation of intense degree of TATE was associated with the absence of distant metastasis, increased lympho-plasmacytic response and increased survival (diseasefree and overall) of OSCC patients. 3) In the subset of 30 cases, tissue eosinophil counts were higher in cases with lymph node involvement, decreased survival, without margin involvement and in cases that did not recur. Conclusion: While the role of eosinophils in mediating immune responses seems ambiguous as eosinophils support cell-mediated tumour immunity in early stages while inhibiting the same in advanced stages, TATE may be used as a surrogate marker for determination of prognosis in oral squamous cell carcinoma.

Keywords: tumour-associated tissue eosinophilia, oral squamous cell carcinoma, prognosticator, tumoral immunity

Procedia PDF Downloads 245

Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

Abstract:

Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

Procedia PDF Downloads 129

Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

Abstract:

The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

Procedia PDF Downloads 71

Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

Abstract:

Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

Procedia PDF Downloads 549

Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

Abstract:

In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

Procedia PDF Downloads 329

Authors: Narin Ozturk, Xiao-Mei Li, Sylvie Giachetti, Francis Levi, Alper Okyar

Abstract:

P-glycoprotein (P-gp, MDR1, ABCB1) is a transmembrane protein acting as an ATP-dependent efflux pump and functions as a biological barrier by extruding drugs and xenobiotics out of cells in healthy tissues especially in intestines, liver and brain as well as in tumor cells. The circadian timing system controls a variety of biological functions in mammals including xenobiotic metabolism and detoxification, proliferation and cell cycle events, and may affect pharmacokinetics, toxicity and efficacy of drugs. Selective mTOR (mammalian target of rapamycin) inhibitor everolimus is an immunosuppressant and anticancer drug that is active against many cancers, and its pharmacokinetics depend on P-gp. The aim of this study was to investigate the dosing time-dependent toxicity of everolimus with respect to the intestinal P-gp expression rhythms in mdr1a::Luc mice using Real Time-Biolumicorder (RT-BIO) System. Mdr1a::Luc male mice were synchronized with 12 h of Light and 12 h of Dark (LD12:12, with Zeitgeber Time 0 – ZT0 – corresponding Light onset). After 1-week baseline recordings, everolimus (5 mg/kg/day x 14 days) was administered orally at ZT1-resting period- and ZT13-activity period- to mdr1a::Luc mice singly housed in an innovative monitoring device, Real Time-Biolumicorder units which let us monitor real-time and long-term gene expression in freely moving mice. D-luciferin (1.5 mg/mL) was dissolved in drinking water. Mouse intestinal mdr1a::Luc oscillation profile reflecting P-gp gene expression and locomotor activity pattern were recorded every minute with the photomultiplier tube and infrared sensor respectively. General behavior and clinical signs were monitored, and body weight was measured every day as an index of toxicity. Drug-induced body weight change was expressed relative to body weight on the initial treatment day. Statistical significance of differences between groups was validated with ANOVA. Circadian rhythms were validated with Cosinor Analysis. Everolimus toxicity changed as a function of drug timing, which was least following dosing at ZT13, near the onset of the activity span in male mice. Mean body weight loss was nearly twice as large in mice treated with 5 mg/kg everolimus at ZT1 as compared to ZT13 (8.9% vs. 5.4%; ANOVA, p < 0.001). Based on the body weight loss and clinical signs upon everolimus treatment, tolerability for the drug was best following dosing at ZT13. Both rest-activity and mdr1a::Luc expression displayed stable 24-h periodic rhythms before everolimus and in both vehicle-treated controls. Real-time bioluminescence pattern of mdr1a revealed a circadian rhythm with a 24-h period with an acrophase at ZT16 (Cosinor, p < 0.001). Mdr1a expression remained rhythmic in everolimus-treated mice, whereas down-regulation was observed in P-gp expression in 2 of 4 mice. The study identified the circadian pattern of intestinal P-gp expression with an unprecedented precision. The circadian timing depending on the P-gp expression rhythms may play a crucial role in the tolerability/toxicity of everolimus. The circadian changes in mdr1a genes deserve further studies regarding their relevance for in vitro and in vivo chronotolerance of mdr1a-transported anticancer drugs. Chronotherapy with P-gp-effluxed anticancer drugs could then be applied according to their rhythmic patterns in host and tumor to jointly maximize treatment efficacy and minimize toxicity.

Keywords: circadian rhythm, chronotoxicity, everolimus, mdr1a::Luc mice, p-glycoprotein

Procedia PDF Downloads 339

Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

Procedia PDF Downloads 184

Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

Abstract:

Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

Procedia PDF Downloads 271

Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

Abstract:

This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

Procedia PDF Downloads 331

Authors: Ritesh Verma, Manisha Rathi, Chand Singh Dhull, Sumit Sachdeva, Jitender Phogat

Abstract:

A Rathke's cleft cyst is a benign growth found on the pituitary gland in the brain, specifically a fluid-filled cyst in the posterior portion of the anterior pituitary gland. It occurs when the Rathke's pouch does not develop properly and ranges in size from 2 to 40mm in diameter. A 38-year-old male presented to the outpatient department with loss of vision in the inferior quadrant of the left eye since 15 days. Visual acuity was 6/6 in the right eye and 6/9 in the left eye. Visual field analysis by HFA-24-2 revealed an inferior field defect extending to the supero-temporal quadrant in the left eye. MRI brain and orbit was advised to the patient and it revealed a well defined cystic pituitary adenoma indenting left optic nerve near optic chiasm consistent with the diagnosis of Rathke’s cleft cyst (RCC). The patient was referred to neurosurgery department for further management. Symptoms vary greatly between individuals having RCCs. RCCs can be non-functioning, functioning, or both. Besides headaches, neurocognitive deficits are almost always present but have a high rate of immediate reversal if the cyst is properly treated or drained.

Keywords: pituitary tumors, rathke’s cleft cyst, visual field defects, vision loss

Procedia PDF Downloads 198

Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

Abstract:

Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

Procedia PDF Downloads 398

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

Abstract:

Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 95

Authors: K. Biswas, U. H. Armin, S. M. J. Prodhan, J. A. Prithul, S. Sarker, F. Afrin

Abstract:

Alzheimer’s disease (AD) (a progressive neurodegenerative disorder) is mostly predominant cause of dementia in the elderly. Prolonging the function of acetylcholine by inhibiting both acetylcholinesterase and butyrylcholinesterase is most effective treatment therapy of AD. Traditionally Pterocarpus santalinus L. is widely known for its medicinal use. In this study, in vitro acetylcholinesterase inhibitory activity was investigated and methanolic extract of the plant showed significant activity. To confirm this activity (in vivo), learning and memory enhancing effects were tested in mice. For the test, memory impairment was induced by scopolamine (cholinergic muscarinic receptor antagonist). Anti-amnesic effect of the extract was investigated by the passive avoidance task in mice. The study also includes brain acetylcholinesterase activity. Results proved that scopolamine induced cognitive dysfunction was significantly decreased by administration of the extract solution, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that bark extract of Pterocarpus santalinus can be better option for further studies on AD via their acetylcholinesterase inhibitory actions.

Keywords: Pterocarpus santalinus, cholinesterase inhibitor, passive avoidance, Alzheimer’s disease

Procedia PDF Downloads 239

Authors: Vishwanath Pethri Kamath, Jayantha Gowda Sarapanahalli, Vishal Mishra, Siddhesh Balwant Bandgar

Abstract:

Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions.

Keywords: text emotion recognition, bidirectional LSTM, multi-head attention, multi-level classification, google word2vec word embeddings

Procedia PDF Downloads 172

Authors: A. Kardi, R. Zagrouba, M. Alqahtani

Abstract:

The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.

Keywords: routing, sensor, survey, wireless sensor networks, WSNs

Procedia PDF Downloads 175

Authors: Pulari C. Milu Maria Anto

Abstract:

Background: Cognitive Rehabilitation/Retraining has been variously used in the research literature to represent non-pharmacological interventions that target the cognitive impairments with the goal of ameliorating cognitive function and functional behaviors to optimize the quality of life. Along with adult’s cognitive impairments, the need to address acquired cognitive impairments (due to any chronic illnesses like CHD - congenital heart diseases or ALL - Acute Lymphoblastic Leukemia) among child populations is inevitable. Also, it has to be emphasized as same we consider the cognitive impairments seen in the children having neurodevelopmental disorders. Methods: All published brain image studies (Hermann, B. et al,2002, Khalil, A. et al., 2004, Follin, C. et al, 2016, etc.) and studies emphasizing cognitive impairments in attention, memory, and/or executive function and behavioral aspects (Henkin, Y. et al,2007, Bellinger, D. C., & Newburger, J. W. (2010), Cheung, Y. T., et al,2016, that could be identified were reviewed. Based on a systematic review of the literature from (2000 -2021) different brain imaging studies, increased risk of neuropsychological and psychosocial impairments are briefly described. Clinical and research gap in the area is discussed. Results:30 papers, both Indian studies and foreign publications (Sage journals, Delhi psychiatry journal, Wiley Online Library, APA PsyNet, Springer, Elsevier, Developmental medicine, and child neurology), were identified. Conclusions: In India, a very limited number of brain imaging studies and neuropsychological studies have done by indicating the cognitive deficits of a child having or undergone chronic illness. None of the studies have emphasized the relevance nor the need of implementingCR among such children, even though its high time to address but still not established yet. The review of the current evidence is to bring out an insight among rehabilitation professionals in establishing a child specific CR and to publish new findings regarding the implementation of CR among such children. Also, this study will be an awareness on considering cognitive aspects of a child having acquired cognitive deficit (due to chronic illness), especially during their critical developmental period.

Keywords: cognitive rehabilitation, neuropsychological impairments, congenital heart diseases, acute lymphoblastic leukemia, epilepsy, and neuroplasticity

Procedia PDF Downloads 174

Authors: Hee Soo Kim, Ha Young Kyung

Abstract:

Happiness and pleasure are a result of dopamine, oxytocin, serotonin, and endorphin levels in the body. In order to increase the four neurochemical levels, it is important to associate daily activities with its corresponding neurochemical releases. This includes setting goals, maintaining social relationships, laughing frequently, and exercising regularly. The likelihood of experiencing happiness increases when all four neurochemicals are released at the optimal level. The achievement of happiness is important because it increases healthiness, productivity, and the ability to overcome adversity. To process emotions, electrical brain waves, brain structure, and neurochemicals must be analyzed. This research uses Chemcraft and Avogadro to determine the theoretical and chemical properties of the four neurochemical molecules. Each neurochemical molecule’s thermodynamic stability is calculated to observe the efficiency of the molecules. The study found that among dopamine, oxytocin, serotonin, alpha-, beta-, and gamma-endorphin, beta-endorphin has the lowest optimized energy of 388.510 kJ/mol. Beta-endorphin, a neurotransmitter involved in mitigating pain and stress, is the most thermodynamically stable and efficient molecule that is involved in the process of happiness. Through examining such properties of happiness neurotransmitters, the science of happiness is better understood.

Keywords: happiness, neurotransmitters, positive psychology, dopamine, oxytocin, serotonin, endorphins

Procedia PDF Downloads 151

Authors: Salma A. El-Marasy, Rehab F. Abdel-Rahman, Reham M. Abd-Elsalam

Abstract:

The burden of stroke is intensely increasing worldwide. Brain injury following transient or permanent focal cerebral ischemia develops ischemic stroke as a consequence of a complex series of pathophysiological events. The aim of this study is to evaluate the possible neuroprotective effect of a dipeptidyl peptidase-4 inhibitor, vildagliptin, independent on its insulinotropic properties in non-diabetic rats subjected to cerebral ischemia. Anaesthetized Wistar rats were subjected to either left middle cerebral artery occlusion (MCAO) or sham operation followed by reperfusion after 30 min of MCAO. The other three groups were orally administered vildagliptin at 3 dose levels (2.5, 5, 10 mg/kg) for 3 successive weeks before subjected to left focal cerebral ischemia/reperfusion and till the end of the study. Neurological deficit scores and motor activity were assessed 24h following reperfusion. 48h following reperfusion, rats were euthanized and their left brain hemispheres were harvested and used in the biochemical, histopathological, and immunohistochemical investigations. Vildagliptin pretreatment improved neurological score deficit, locomotor activity and motor coordination in MCAO rats. Moreover, vildagliptin reduced malondialdehyde (MDA), elevated reduced glutathione (GSH), phosphotylinosital 3 kinase (PI3K), phosphorylated of protein kinase B (p-AKT), and mechanistic target of rapamycin (mTOR) brain contents in addition to reducing protein expression of caspase-3. Also, vildagliptin showed a dose-dependent attenuation in neuronal cell loss and histopathological alterations in MCAO rats. This study proves that vildagliptin exerted the neuroprotective effect in a dose-dependent manner as shown in amelioration of neuronal cell loss and histopathological damage in MCAO rats, which may be mediated by attenuating neuronal and motor deficits, it’s anti-oxidant property, activation of PI3K/AKT/mTOR pathway and its anti-apoptotic effect.

Keywords: caspase-3, cerebral ischemia, dipeptidyl peptidase-4 inhibitor, oxidative stress, PI3K/AKT/mTOR pathway, rats, vildagliptin

Procedia PDF Downloads 152

Authors: Kuplevatsky V., Kuplevatskay, Cherkashin M., Berezina N.

Abstract:

After 6 months, a violation of the differentiation of the structure of the gland due to edema in 100%. 20% retained signs of a tumor according to DWI/ADC data. By 12 months, the reduction in the size of the gland is 100%. In all cases, no diffusion restriction was observed. The study after 18 months showed no significant changes in all (100%) patients. In the study, 24 months after treatment, the size of the gland was stable in all cases (+/- up to 5%). Diffuse decrease in T2VI signals from peripheral zones, without signs of diffusion restriction in 100%. After 30 months, signs of recovery of adenomatous changes in the transient zone were revealed in 85%. After 36 and 42 months, the restoration of organ differentiation was observed in 93% of patients. In 4 patients, by the 48th month, signs of biochemical relapse were clinically noted. According to the MRI data, signs of a local relapse were revealed. After 48 months, there were signs of restoration of organ differentiation, which allowed the use of PI-RADS criteria. The study after 54 months showed no changes compared to the control. 60 months after treatment, 97% of patients showed a restoration of differentiation of the gland structure, which allows evaluating the organ according to PI-RADS criteria Conclusions: The beginning of restoration of the structure of the prostate gland began 24 months after proton radiation therapy, the PI-RADS criteria can be fully applied after 48 months of treatment. Control studies every 6 months without clinical signs of relapse are not advisable. Local control of the prostate tumor after proton radiation therapy was achieved in 95% of patients during the entire follow-up period ( 60 months).

Keywords: proton therapy, prostate cancer, MRI imaging, PI-RADS

Procedia PDF Downloads 98

Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

Abstract:

Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

Procedia PDF Downloads 65

Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

Abstract:

This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

Procedia PDF Downloads 469

Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

Abstract:

This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

Procedia PDF Downloads 456

Authors: Chiling Chen, Chiaoying Chou, Siyang Wu

Abstract:

Taiwan locates at the west of Pacific Ocean and intersects between continental and marine climate. Typhoons frequently strike Taiwan and come with meteorological disasters, i.e., heavy flooding, landslides, loss of life and properties, etc. Global climate change brings more extremely meteorological disasters. So, develop techniques to improve disaster prevention and mitigation is needed, to improve rescue processes and rehabilitations is important as well. In this study, UAVs (Unmanned Aerial Vehicles) are applied to take instant images for improving the disaster investigation and rescue processes. Paddy rice fields in the central Taiwan are the study area. There have been attacked by heavy rain during the monsoon season in June 2016. UAV images provide the high ground resolution (3.5cm) with 3D Point Clouds to develop image discrimination techniques and digital surface model (DSM) on rice lodging. Firstly, image supervised classification with Maximum Likelihood Method (MLD) is used to delineate the area of rice lodging. Secondly, 3D point clouds generated by Pix4D Mapper are used to develop DSM for classifying the lodging levels of paddy rice. As results, discriminate accuracy of rice lodging is 85% by image supervised classification, and the classification accuracy of lodging level is 87% by DSM. Therefore, UAVs not only provide instant images of agricultural damage after the meteorological disaster, but the image discriminations on rice lodging also reach acceptable accuracy (>85%). In the future, technologies of UAVs and image discrimination will be applied to different crop fields. The results of image discrimination will be overlapped with administrative boundaries of paddy rice, to establish GIS-based assist system on agricultural damage discrimination. Therefore, the time and labor would be greatly reduced on damage detection and monitoring.

Keywords: Monsoon, supervised classification, Pix4D, 3D point clouds, discriminate accuracy

Procedia PDF Downloads 298

Authors: Arfan Ali Nagra, Tariq Shahzad, Meshal Alharbi, Khalid Masood Khan, Muhammad Mugees Asif, Taher M. Ghazal, Khmaies Ouahada

Abstract:

Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify a number of genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Keywords: microarray cancer, improved PSO, ELM, SVM, evolutionary algorithms

Procedia PDF Downloads 80

Authors: Alexandra K. Diem, Giles Richardson, Roxana O. Carare, Neil W. Bressloff

Abstract:

Alzheimer's disease (AD) is the most common form of dementia and although it has been researched for over 100 years, there is still no cure or preventive medication. Its onset and progression is closely related to the accumulation of the neuronal metabolite Aβ. This raises the question of how metabolites and waste products are eliminated from the brain as the brain does not have a traditional lymphatic system. In recent years the rapid uptake of Aβ into cerebral artery walls and its clearance along those arteries towards the lymph nodes in the neck has been suggested and confirmed in mice studies, which has led to the hypothesis that interstitial fluid (ISF), in the basement membranes in the walls of cerebral arteries, provides the pathways for the lymphatic drainage of Aβ. This mechanism, however, requires a net reverse flow of ISF inside the blood vessel wall compared to the blood flow and the driving forces for such a mechanism remain unknown. While possible driving mechanisms have been studied using mathematical models in the past, a mechanism for net reverse flow has not been discovered yet. Here, we aim to address the question of the driving force of this reverse lymphatic drainage of Aβ (also called perivascular drainage) by using multi-scale numerical and analytical modelling. The numerical simulation software COMSOL Multiphysics 4.4 is used to develop a fluid-structure interaction model of a cerebral artery, which models blood flow and displacements in the artery wall due to blood pressure changes. An analytical model of a layer of basement membrane inside the wall governs the flow of ISF and, therefore, solute drainage based on the pressure changes and wall displacements obtained from the cerebral artery model. The findings suggest that an active role in facilitating a reverse flow is played by the components of the basement membrane and that stiffening of the artery wall during age is a major risk factor for the impairment of brain lymphatics. Additionally, our model supports the hypothesis of a close association between cerebrovascular diseases and the failure of perivascular drainage.

Keywords: Alzheimer's disease, artery wall mechanics, cerebral blood flow, cerebral lymphatics

Procedia PDF Downloads 522

Authors: Michal Krecichwost, Zauzanna Miodonska, Pawel Badura

Abstract:

The diagnosis of sigmatism is mostly based on the observation of articulatory organs. It is, however, not always possible to precisely observe the vocal apparatus, in particular in the oral cavity of the patient. Speech processing can allow to objectify the therapy and simplify the verification of its progress. In the described study the methodology for classification of incorrectly pronounced phoneme [s] is proposed. The recordings come from adults. They were registered with the speech recorder at the sampling rate of 44.1 kHz and the resolution of 16 bit. The database of pathological and normative speech has been collected for the study including reference assessments provided by the speech therapy experts. Ten adult subjects were asked to simulate a certain type of stigmatism under the speech therapy expert supervision. In the recordings, the analyzed phone [s] was surrounded by vowels, viz: ASA, ESE, ISI, SPA, USU, YSY. Thirteen MFCC (mel-frequency cepstral coefficients) and RMS (root mean square) values are calculated within each frame being a part of the analyzed phoneme. Additionally, 3 fricative formants along with corresponding amplitudes are determined for the entire segment. In order to aggregate the information within the segment, the average value of each MFCC coefficient is calculated. All features of other types are aggregated by means of their 75th percentile. The proposed method of features aggregation reduces the size of the feature vector used in the classification. Binary SVM (support vector machine) classifier is employed at the phoneme recognition stage. The first group consists of pathological phones, while the other of the normative ones. The proposed feature vector yields classification sensitivity and specificity measures above 90% level in case of individual logo phones. The employment of a fricative formants-based information improves the sole-MFCC classification results average of 5 percentage points. The study shows that the employment of specific parameters for the selected phones improves the efficiency of pathology detection referred to the traditional methods of speech signal parameterization.

Keywords: computer-aided pronunciation evaluation, sibilants, sigmatism diagnosis, speech processing

Procedia PDF Downloads 276

Authors: Paweł Konieczka, Lech Raczyński, Wojciech Wiślicki, Oleksandr Fedoruk, Konrad Klimaszewski, Przemysław Kopka, Wojciech Krzemień, Roman Shopa, Jakub Baran, Aurélien Coussat, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Meysam Dadgar, Kamil Dulski, Aleksander Gajos, Beatrix C. Hiesmayr, Krzysztof Kacprzak, łukasz Kapłon, Grzegorz Korcyl, Tomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Dominik Panek, Szymon Parzych, Elena Pérez Del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa łucja Stępień, Faranak Tayefi, Paweł Moskal

Abstract:

This paper develops the transformation of non-image data into 2-dimensional matrices, as a preparation stage for classification based on convolutional neural networks (CNNs). In positron emission tomography (PET) studies, CNN may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, much PET data still exists in non-image format and this fact opens a question on whether they can be used for training CNN. In this contribution, the main focus of this paper is the problem of processing vectors with a small number of features in comparison to the number of pixels in the output images. The proposed methodology was applied to the classification of PET coincidence events.

Keywords: convolutional neural network, kernel principal component analysis, medical imaging, positron emission tomography

Procedia PDF Downloads 137

Authors: Naim Izet Kajtazi

Abstract:

Context: The third ventricle colloid cyst (CC) is a benign growth usually located in the third ventricle and can cause various neurological symptoms, including sudden death. Modern surgical interventions may still result in a wide range of complications and cerebral venous thrombosis (CVT) is among them. Process: A 38-year-old female with an existing diagnosis of diabetes mellitus (DM) and hypothyroidism and a six-month history of headaches, blurred vision, and vomiting presented to our clinic three days after the headaches became excessively severe. Neurological examination on admission revealed bilateral papilledema without any associated focal neurological deficits. Brain computed tomography (CT) and magnetic resonance imaging (MRI) confirmed the presence of a third ventricle colloid cyst and associated non-communicating hydrocephalus involving the lateral ventricles. As a result, the patient underwent emergency bilateral external ventricular drainage (EVD) insertion followed by a third ventricular CC excision under neuronavigation through a right frontal craniotomy. Twelve days post-operatively, the patient developed further headaches, followed by a generalized tonic-clonic seizure that led to no postictal neurological deficits. Nonetheless, computed tomography venography of the brain revealed extensive thrombosis of the superior sagittal sinus, inferior sagittal sinus, right sigmoid sinus, and right internal jugular vein. A newly diagnosed CVT was treated with intravenous heparin. The patient was discharged with warfarin, which was discontinued after 12 months. Ten years after her illness, she remained stable and free from any neurological deficits but still suffered from mild chronic headaches. Outcome: Ten years after her illness, she remained stable and free from any neurological deficits but still suffered from mild chronic headaches. Relevance: A preoperative venous study should be performed in all cases to gain a better understanding of the venous anatomy. We advocate meticulous microsurgical techniques to protect the venous system surrounding the foramen of Monro and reduce the amount of retraction during surgery.

Keywords: CVT, seizures, third ventricle colloid cyst, MRI of brain

Procedia PDF Downloads 61

Authors: Tomás Sobrino, Lara García-Varela, Marta Aramburu-Núñez, Mónica Castro, Noemí Gómez-Lado, Mariña Rodríguez-Arrizabalaga, Antía Custodia, Juan Manuel Pías-Peleteiro, José Manuel Aldrey, Daniel Romaus-Sanjurjo, Ángeles Almeida, Pablo Aguiar, Alberto Ouro

Abstract:

Introduction: Alzheimer’s disease (AD) and other tauopathies are primary causes of dementia, causing progressive cognitive deterioration that entails serious repercussions for the patients' performance of daily tasks. Currently, there is no effective approach for the early diagnosis and treatment of AD and tauopathies. This study suggests a theragnostic approach based on the importance of phosphorylated tau protein (p-Tau) in the early pathophysiological processes of AD. We have developed a novel theragnostic monoclonal antibody (mAb) to provide both diagnostic and therapeutic effects. Methods/Results: We have developed a p-Tau mAb, which was doped with deferoxamine for radiolabeling with Zirconium-89 (89Zr) for PET imaging, as well as fluorescence dies for immunofluorescence assays. The p-Tau mAb was evaluated in vitro for toxicity by MTT assay, LDH activity, propidium iodide/Annexin V assay, caspase-3, and mitochondrial membrane potential (MMP) assay in both mouse endothelial cell line (bEnd.3) and cortical primary neurons cell cultures. Importantly, non-toxic effects (up to concentrations of p-Tau mAb greater than 100 ug/mL) were detected. In vivo experiments in the tauopathy model mice (PS19) show that the 89Zr-pTau-mAb and 89Zr-Fragments-pTau-mAb are stable in circulation for up to 10 days without toxic effects. However, only less than 0.2% reached the brain, so further strategies have to be designed for crossing the Brain-Blood-Barrier (BBB). Moreover, an intraparenchymal treatment strategy was carried out. The PS19 mice were operated to implement osmotic pumps (Alzet 1004) at two different times, at 4 and 7 months, to stimulate the controlled release for one month each of the B6 antibody or the IgG1 control antibody. We demonstrated that B6-treated mice maintained their motor and memory abilities significantly compared with IgG1 treatment. In addition, we observed a significant reduction in p-Tau deposits in the brain. Conclusions /Discussion: A theragnostic pTau-mAb was developed. Moreover, we demonstrated that our p-Tau mAb recognizes very-early pathology forms of p-Tau by non-invasive techniques, such as PET. In addition, p-Tau mAb has non-toxic effects, both in vitro and in vivo. Although the p-Tau mAb is stable in circulation, only 0.2% achieve the brain. However, direct intraventricular treatment significantly reduces cognitive impairment in Alzheimer's animal models, as well as the accumulation of toxic p-Tau species.

Keywords: alzheimer's disease, theragnosis, tau, PET, immunotherapy, tauopathies

Procedia PDF Downloads 64

Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

Abstract:

In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

Procedia PDF Downloads 426