Search results for: Newcastle disease model

Authors: Ecem Deniz Kırkpantur, Ozge Ecmel Onur, Tuba Cimilli Ozturk, Ebru Unal Akoglu

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Moyamoya disease is a unique chronic progressive cerebrovascular disease characterized by bilateral stenosis or occlusion of the arteries around the circle of Willis with prominent arterial collateral circulation. The occurrence of Moyamoya disease is related to immune, genetic and other factors. There is no curative treatment for Moyamoya disease. Secondary prevention for patients with symptomatic Moyamoya disease is largely centered on surgical revascularization techniques. We present here a 62-year old male presented with headache and vision loss for 2 days. He was previously diagnosed with hypertension and glaucoma. On physical examination, left eye movements were restricted medially, both eyes were hyperemic and their movements were painful. Other neurological and physical examination were normal. His vital signs and laboratory results were within normal limits. Computed tomography (CT) showed dilated vascular structures around both lateral ventricles and atherosclerotic changes inside the walls of internal carotid artery (ICA). Magnetic resonance imaging (MRI) and angiography (MRA) revealed dilated venous vascular structures around lateral ventricles and hyper-intense gliosis in periventricular white matter. Ischemic gliosis around the lateral ventricles were present in the Digital Subtracted Angiography (DSA). After the neurology, ophthalmology and neurosurgery consultation, the patient was diagnosed with Moyamoya disease, pulse steroid therapy was started for vision loss, and super-selective DSA was planned for further investigation. Moyamoya disease is a rare condition, but it can be an important cause of stroke in both children and adults. It generally affects anterior circulation, but posterior cerebral circulation may also be affected, as well. In the differential diagnosis of acute vision loss, occipital stroke related to Moyamoya disease should be considered. Direct and indirect surgical revascularization surgeries may be used to effectively revascularize affected brain areas, and have been shown to reduce risk of stroke.

Keywords: headache, Moyamoya disease, stroke, visual loss

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Authors: Shripad Kulkarni

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Plant disease is an impairment of the normal state of a plant that interrupts or modifies its vital functions. Disease occurs on different parts of plants and cause heavy losses. Diagnosis of Problem is very important before planning any management practice and this can be done based on appearance of the crop, examination of the root and examination of the soil. There are various types of diseases such as biotic (transmissible) which accounts for ~30% whereas , abiotic (not transmissible) diseases are the major one with ~70% incidence. Plant diseases caused by different groups of organism’s belonging fungi, bacteria, viruses, nematodes and few others have remained important in causing significant losses in different crops indicating the urgent need of their integrated management. Various factors favor disease development and different steps and methods are involved in management of diseases under protected condition. Management of diseases through botanicals and bioagents by modifying root and aerial environment, vector management along with care to be taken while managing the disease are analysed.

Keywords: organic production system, diseases, bioagents and polyhouse, agriculture

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Authors: Yuxi Wang, Xuan Gao

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Traditional Chinese medicine has been used in the treatment of coronary heart disease (CHD) for centuries, and in recent years, the research data on the efficacy of traditional Chinese medicine through clinical trials has gradually increased to explore its real efficacy and internal pharmacology. However, due to the complexity of traditional Chinese medicine prescriptions, the efficacy of each component is difficult to clarify, and pharmacological research is challenging. This study aims to systematically review and clarify the clinical efficacy of traditional Chinese medicine in the treatment of coronary heart disease through a meta-analysis. Based on PubMed, CNKI database, Wanfang data, and other databases, eleven randomized controlled trials and 1091 CHD subjects were included. Two researchers conducted a systematic review of the papers and conducted a meta-analysis supporting the positive therapeutic effect of traditional Chinese medicine in the treatment of CHD.

Keywords: coronary heart disease, Chinese medicine, treatment, meta-analysis

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Authors: Thananyada Buapian

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Overnutrition is emerging as a morbid disease in developing and Westernized countries. Because of its comorbidity diseases, it is cost-effective to prevent and manage this disease earlier. In Thailand, this alarming disease has long been studied, but the prevalence is still higher than that in the past. Physicians should recognize it well and have a definite direction to face and combat this dangerous disease. Rapid changes in the tremendous figure of overnutrition students indicate that genetic factors are not the primary determinants since human genes have remained unchanged for a century. This study aims to assess the prevalence of overnutrition students and to investigate the non-genetic factors affecting over nutrition students. A cross-sectional school-based survey was conducted. A two-stage sampling was adopted. Respondents included 1,850 students in grades 4 to 6 in Ayutthaya Province. An anthropometric measurement and questionnaire were developed. Childhood over nutrition was defined as a weight-for-height Z-score above +2SD of NCHS/WHO references. About thirty three percent of the children were over nutrition in Ayutthaya province. Stepwise multiple logistic regression analysis showed that 8 statistically significant non genetic factors explain the variation of childhood over nutrition by 18 percent. Sex is the prime factor to explain the variation of childhood over nutrition, followed by duration of light physical activities, duration of moderate physical activities, having been breastfed, the presence of a healthy role model of the caregiver, number of siblings, birth order, and occupation of the caregiver, respectively. Non genetic factors, especially the subjects’ demographic and physical activities, as well as the caregivers’ background and family environment, should be considered in viable approach to remedy this health imbalance in children.

Keywords: non genetic factors, non-genetic, over nutrition, over nutrition students

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Authors: Mahzad Yousefi, Azin Tavakoli

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Periodontal disease is an inflammatory reaction; therefore, it is predicted that changes may occur in some inflammatory parameters that can be detected in routine blood tests. The objective of this study was to evaluate the hematological and biochemistry changes that occur in dogs affected with advanced stages of periodontal disease. 87 dogs were diagnosed with periodontal disease (PD group), and 76 healthy dogs entered the study. The PD dogs had been affected with periodontitis stage 3 or 4 and were candidates for any dental extractions. The healthy dogs were either referred for annual checkups or for issuing health travel certificates that their owners asked for complete lab tests. Neither the diseased nor healthy subjects had a history of infectious, or other general health problems or surgery in the past 3 months. Age, as well as all hematologic including PCV, WBC and RBC count, Hb, MCV, MCH, MCHC, PLT, CBC, NLR, and biochemistry data, including total protein, albumin, glucose, BUN, Creatinine, ALT, AST, and ALP, were recorded and analyzed statistically. Results confirmed that aging has a significant direct relationship with the advanced stages of periodontal disease. Mild leukocytosis occurred in the diseased group; however, it was not significant. Also, the mean total protein of the PD group was lower than that of the healthy dogs, and serum levels of albumin were found to be lower significantly in the diseased group (P<0.05). Mean ±SD amount of Platelet, MCH, and ALT were significantly higher in the diseased group in comparison to the healthy dogs (P<0.05). No significant differences were reported in other evaluated parameters. It is concluded that CBC indices of PD dogs are not systemic inflammatory; however, only a decrease in albumin showed inflammatory responses. Some indices in routine laboratory tests can be changed significantly during advanced stages of the periodontal disease dogs.

Keywords: periodontal disease, dogs, hematological factors, advanced stages, blood tests

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Authors: Małgorzata Tomczyńska, Joanna Saluk-Bijak

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Background: Graves’ disease is a frequent organ-specific autoimmune thyroid disease, which characterized by the presence of different kind autoantibodies, that, in most cases, act as agonists of the thyrotropin receptor, leading to hyperthyroidism. Role of platelets and lymphocytes can be modulated in the pathophysiology of thyroid autoimmune diseases. Interference in the physiology of platelets can lead to enhanced activity of these cells. Activated platelets can bind to circulating lymphocytes and to affect lymphocyte adhesion. Platelets and lymphocytes can regulate mutual functions. Therefore, the activation of T lymphocytes, as well as blood platelets, is associated with the development of inflammation and oxidative stress within the target tissue. The present study was performed to investigate a platelet-lymphocyte relation by assessing the degree of their mutual aggregation in whole blood of patients with Graves’ disease. Also, the purpose of this study was to examine the impact of platelet interaction on lymphocyte migration capacity. Methods: 30 patients with Graves’ disease were recruited in the study. The matched 30 healthy subjects were served as the control group. Immunophenotyping of lymphocytes was carried out by flow cytometry method. A CytoSelect™ Cell Migration Assay Kit was used to evaluate lymphocyte migration and adhesion to blood platelets. Visual assessment of lymphocyte-platelet aggregate morphology was done using confocal microscope after magnetic cell isolation by Miltenyi Biotec. Results: The migration and functional responses of lymphocytes to blood platelets were greater in the group of Graves’ disease patients compared with healthy controls. The group of Graves’ disease patients exhibited a reduced T lymphocyte and a higher B cell count compared with controls. Based on microscopic analysis, more platelet-lymphocyte aggregates were found in patients than in control. Conclusions: Studies have shown that in Graves' disease, lymphocytes show increased platelet affinity, more strongly migrating toward them, and forming mutual cellular conglomerates. This may be due to the increased activation of blood platelets in this disease.

Keywords: blood platelets, cell migration, Graves’ disease, lymphocytes, lymphocyte-platelet aggregates

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: Mahsa Hadipour Jahromy, Sara Rezaii

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Parkinson’s disease (PD) results primarily from the death of dopaminergic neurons in the substantia nigra. Soon after the discovery of levodopa and its beneficial effects in chronic administration, debilitating involuntary movements observed, termed levodopa-induced dyskinesia (LID) with poorly understood pathogenesis. Polyphenol-rich compounds, like pomegranate, provided neuroprotection in several animal models of brain diseases. In the present work, we investigated whether pomegranate has preventive effects following 4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic degenerations and the potential to diminish LID in mice. Mice model of PD was induced by MPTP (30 mg/kg daily for five consecutive days). To induce a mice model of LID, valid PD mice were treated with levodopa (50 mg/kg, i.p) for 15 days. Then the effects of chronic co-administration of pomegranate juice (20 ml/kg) with levodopa and continuing for 10 days, evaluated. Behavioural tests were performed in all groups, every other day including: Abnormal involuntary movements (AIMS), forelimb adjusting steps, cylinder, and catatonia tests. Finally, brain tissue sections were prepared to study substantia nigra changes and dopamine neuron density after treatments. With this MPTP regimen, significant movement disorders revealed in AIMS tests and there was a reduction in dopamine striatal density. Levodopa attenuates their loss caused by MPTP, however, in chronic administration, dyskinesia observed in forelimb adjusting step and cylinder tests. Besides, catatonia observed in some cases. Chronic pomegranate co-administration significantly improved LID in both tests and reduced dopaminergic loss in substantia nigra. These data indicate that pomegranate might be a good adjunct for preserving dopaminergic neurons in the substantia nigra and reducing LID in mice.

Keywords: levodopa-induced dyskinesia, MPTP, Parkinson’s disease, pomegranate

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Authors: Selvaprakash Ramalingam, Rabi N. Sahoo, Dharmendra Saraswat, A. Kumar, Rajeev Ranjan, Joydeep Mukerjee, Viswanathan Chinnasamy, K. K. Chaturvedi, Sanjeev Kumar

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Rice is one of the most important staple food crops in the world. Among the various diseases that affect rice crops, rice blast is particularly significant, causing crop yield and economic losses. While the plant has defense mechanisms in place, such as chemical indicators (proteins, salicylic acid, jasmonic acid, ethylene, and azelaic acid) and resistance genes in certain varieties that can protect against diseases, susceptible varieties remain vulnerable to these fungal diseases. Early prediction of rice blast (RB) disease is crucial, but conventional techniques for early prediction are time-consuming and labor-intensive. Hyperspectral remote sensing techniques hold the potential to predict RB disease at its asymptomatic stage. In this study, we aimed to demonstrate the prediction of RB disease at the asymptomatic stage using non-imaging hyperspectral ASD spectroradiometer under controlled laboratory conditions. We applied statistical spectral discrimination theory to identify unknown spectra of M. Oryzae, the fungus responsible for rice blast disease. The infrared (IR) region was found to be significantly affected by RB disease. These changes may result in alterations in the absorption, reflection, or emission of infrared radiation by the affected plant tissues. Our research revealed that the protein spectrum in the IR region is impacted by RB disease. In our study, we identified strong correlations in the region (Amide group - I) around X 1064 nm and Y 1300 nm with the Lambda / Lambda derived spectra methods for protein detection. During the stages when the disease is developing, typically from day 3 to day 5, the plant's defense mechanisms are not as effective. This is especially true for the PB-1 variety of rice, which is highly susceptible to rice blast disease. Consequently, the proteins in the plant are adversely affected during this critical time. The spectral contour plot reveals the highly correlated spectral regions 1064 nm and Y 1300 nm associated with RB disease infection. Based on these spectral sensitivities, we developed new spectral disease indices for predicting different stages of disease emergence. The goal of this research is to lay the foundation for future UAV and satellite-based studies aimed at long-term monitoring of RB disease.

Keywords: rice blast, asymptomatic stage, spectral sensitivity, IR

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Anat Achiron

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Background: Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system in young adults. It involves the immune system attacking the protective covering of nerve fibers (myelin), leading to inflammation and damage. MS can result in various neurological symptoms, such as muscle weakness, coordination problems, and sensory disturbances. Seizures are not common in MS, and the frequency is estimated between 0.4 to 6.4% over the disease course. Objective: Investigate the frequency of seizures in individuals with multiple sclerosis and to identify associated risk factors. Methods: We evaluated the frequency of seizures in a large cohort of 5686 MS patients followed at the Sheba Multiple Sclerosis Center and studied associated risk factors and comorbidities. Our research was based on data collection using a cohort study design. We applied logistic regression analysis to assess the strength of associations. Results: We found that younger age at onset, longer disease duration, and prolonged time to immunomodulatory treatment initiation were associated with increased risk for seizures. Conclusions: Our findings suggest that seizures in people with MS are directly related to the demyelination process and not associated with other factors like medication side effects or comorbid conditions. Therefore, initiating immunomodulatory treatment early in the disease course could reduce not only disease activity but also decrease seizure risk.

Keywords: epilepsy, seizures, multiple sclerosis, white matter, age

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Authors: Nursel Koyuncu, Nihal Ata Tutkun

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Frailty model is a survival model that takes into account the unobserved heterogeneity for exploring the relationship between the survival of an individual and several covariates. In the recent years, proposed survival models become more complex and this feature causes convergence problems especially in large data sets. Therefore selection of sample from these big data sets is very important for estimation of parameters. In sampling literature, some authors have defined new sampling schemes to predict the parameters correctly. For this aim, we try to see the effect of sampling design in semi-parametric frailty model. We conducted a simulation study in R programme to estimate the parameters of semi-parametric frailty model for different sample sizes, censoring rates under classical simple random sampling and ranked set sampling schemes. In the simulation study, we used data set recording 17260 male Civil Servants aged 40–64 years with complete 10-year follow-up as population. Time to death from coronary heart disease is treated as a survival-time and age, systolic blood pressure are used as covariates. We select the 1000 samples from population using different sampling schemes and estimate the parameters. From the simulation study, we concluded that ranked set sampling design performs better than simple random sampling for each scenario.

Keywords: frailty model, ranked set sampling, efficiency, simple random sampling

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Xianpei Li, Yanmin Yuan, Tracy Lin Huan

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Researchers are doing enchanting research into the inherited features of Alzheimer’s disease and probable consistent therapies. In Alzheimer’s, memories are extinct in reverse order; memories formed lately are more transitory than those from formerly. Reminiscence therapy includes the conversation of past actions, trials and knowledges with another individual or set of people, frequently with the help of perceptible reminders such as photos, household and other acquainted matters from the past, music and collection of tapes. In this manuscript, the competence of reminiscence therapy for Alzheimer’s disease is measured using logistic regression based linear bootstrap aggregating. Logistic regression is used to envisage the experiential features of the patient’s memory through various therapies. Linear bootstrap aggregating shows better stability and accuracy of reminiscence therapy used in statistical classification and regression of memories related to validation therapy, supportive psychotherapy, sensory integration and simulated presence therapy.

Keywords: Alzheimer’s disease, linear bootstrap aggregating, logistic regression, reminiscence therapy

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Mualla McManus, Jenna Luche Thaye

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World Health Organisation International Classification of Disease codes were created to define disease including infections in order to guide and educate diagnosticians. Most infectious diseases such as syphilis are clearly defined by their ICD 10 codes and aid/help to educate the clinicians in syphilis diagnosis and treatment globally. However, current ICD 10 codes for relapsing fever Borreliosis and Lyme disease are less clearly defined and can impede appropriate diagnosis especially if the clinician is not familiar with the symptoms of these infectious diseases. This is despite substantial number of scientific articles published in peer-reviewed journals about relapsing fever and Lyme disease. In the USA there are estimated 380,000 people annually contacting Lyme disease, more cases than breast cancer and 6x HIV/AIDS cases. This represents estimated 0.09% of the USA population. If extrapolated to the global population (7billion), 0.09% equates to 63 million people contracting relapsing fever or Lyme disease. In many regions, the rate of contracting some form of infection from tick bite may be even higher. Without accurate and appropriate diagnostic codes, physicians are impeded in their ability to properly care for their patients, leaving those patients invisible and marginalized within the medical system and to those guiding public policy. This results in great personal hardship, pain, disability, and expense. This unnecessarily burdens health care systems, governments, families, and society as a whole. With accurate diagnostic codes in place, robust data can guide medical and public health research, health policy, track mortality and save health care dollars. Better defined ICD codes are the way forward in educating the diagnosticians about relapsing fever and Lyme diseases.

Keywords: WHO ICD codes, relapsing fever, Lyme diseases, World Health Organisation

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Saida Haider, Syeda Madiha

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Parkinson’s disease (PD) is a common neurological disorder characterized by motor deficits and loss of dopaminergic neurons. Oxidative stress is said to play a pivotal role in the pathophysiology of the disease. In the present study, PD was induced by injection of rotenone (1.5 mg/kg/day, s.c.) for eight days. Pistachio (800 mg/kg/day, p.o.) was given for two weeks. At the end of treatment brains were dissected out and striatum was isolated for biochemical and neurochemical analysis. Morris water maze (MWM) test and novel object recognition (NOR) task was used to test the memory function while motor behavior was determined by open field test (OFT), Kondziela inverted screen test (KIST), pole test (PT), beam walking test (BWT), inclined plane test (IPT) and footprint (FP) test. Several dietary components have been evaluated as potential therapeutic compounds in many neurodegenerative diseases. Increasing evidence shows that nuts have protective effects against various diseases by improving the oxidative status and reducing lipid peroxidation. Pistachio is the only nut that contains anthocyanin, a potent antioxidant having neuroprotective properties. Results showed that pistachio supplementation significantly restored the rotenone-induced motor deficits and improved the memory performance. Moreover, rats treated with pistachio also exhibited enhanced oxidative status and increased dopamine (DA) and 5-hydroxytryptamine (5-HT) concentration in striatum. In conclusion, to our best knowledge, we have for the first time shown that pistachio nut possesses neuroprotective effects against rotenone-induced motor and cognitive deficits. These beneficial effects of pistachio may be attributed to its high content of natural antioxidant and phenolic compounds. Hence, consumption of pistachio regularly as part of a daily diet can be beneficial in the prevention and treatment of PD.

Keywords: rotenone, pistachio, oxidative stress, Parkinson’s disease

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Authors: Dulal Bhaumik, Fei Jie, Runa Bhaumik, Bikas Sinha

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Human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and autism. fMRI has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose mixed-effects models together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities in whole brain studies. Results are illustrated with a large data set known as Autism Brain Imaging Data Exchange or ABIDE which includes 361 subjects from 8 medical centers. We believe that our findings have addressed adequately the small sample inference problem, and thus are more reliable for therapeutic target for intervention. In addition, our result can be used for early detection of subjects who are at high risk of developing neurological disorders.

Keywords: ABIDE, autism spectrum disorder, fMRI, mixed-effects model

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Authors: Chen Li-Ching

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The multinomial logistic regression model is used popularly for inferring the relationship of risk factors and disease with multiple categories. This study based on the discrepancy between the nonparametric maximum likelihood estimator and semiparametric maximum likelihood estimator of the cumulative distribution function to propose a Kolmogorov-Smirnov type test statistic to assess adequacy of the multinomial logistic regression model for case-control data. A bootstrap procedure is presented to calculate the critical value of the proposed test statistic. Empirical type I error rates and powers of the test are performed by simulation studies. Some examples will be illustrated the implementation of the test.

Keywords: case-control studies, goodness-of-fit test, Kolmogorov-Smirnov test, multinomial logistic regression

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Authors: Jyotismita Talukdar

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In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free environment and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient’s data has been collected and studied. The data is analysed and the results have been compared with the Doctor’s diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). It has been found that highest accuracy(93%), has been achieved in case of PPI( Post-Permanent-Pacemaker Implementation ), around 79% in case of CAD(Coronary Artery disease), 87% in DCM (Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75 % in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP (Ischemic Cardiomyopathy), about 38% and AF( Atrial Fibrillation), about 60 to 62%.

Keywords: coronary heart disease, chronic stable angina, sick sinus syndrome, cardiovascular disease, cholesterol, Thalach

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Authors: T. Heikkinen, J. Oksman, T. Bragge, A. Nurmi, O. Kontkanen, T. Ahtoniemi

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Motor impairment is an inherent phenotypic feature of several chronic neurodegenerative diseases, and pharmacological therapies aimed to counterbalance the motor disability have a great market potential. Animal models of chronic neurodegenerative diseases display a number deteriorating motor phenotype during the disease progression. There is a wide array of behavioral tools to evaluate motor functions in rodents. However, currently existing methods to study motor functions in rodents are often limited to evaluate gross motor functions only at advanced stages of the disease phenotype. The most commonly applied traditional motor assays used in CNS rodent models, lack the sensitivity to capture fine motor impairments or improvements. Fine motor skill characterization in rodents provides a more sensitive tool to capture more subtle motor dysfunctions and therapeutic effects. Importantly, similar approach, kinematic movement analysis, is also used in clinic, and applied both in diagnosis and determination of therapeutic response to pharmacological interventions. The aim of this study was to apply kinematic gait analysis, a novel and automated high precision movement analysis system, to characterize phenotypic deficits in three different chronic neurodegenerative animal models, a transgenic mouse model (SOD1 G93A) for amyotrophic lateral sclerosis (ALS), and R6/2 and Q175KI mouse models for Huntington’s disease (HD). The readouts from walking behavior included gait properties with kinematic data, and body movement trajectories including analysis of various points of interest such as movement and position of landmarks in the torso, tail and joints. Mice (transgenic and wild-type) from each model were analyzed for the fine motor kinematic properties at young ages, prior to the age when gross motor deficits are clearly pronounced. Fine motor kinematic Evaluation was continued in the same animals until clear motor dysfunction with conventional motor assays was evident. Time course analysis revealed clear fine motor skill impairments in each transgenic model earlier than what is seen with conventional gross motor tests. Motor changes were quantitatively analyzed for up to ~80 parameters, and the largest data sets of HD models were further processed with principal component analysis (PCA) to transform the pool of individual parameters into a smaller and focused set of mutually uncorrelated gait parameters showing strong genotype difference. Kinematic fine motor analysis of transgenic animal models described in this presentation show that this method isa sensitive, objective and fully automated tool that allows earlier and more sensitive detection of progressive neuromuscular and CNS disease phenotypes. As a result of the analysis a comprehensive set of fine motor parameters for each model is created, and these parameters provide better understanding of the disease progression and enhanced sensitivity of this assay for therapeutic testing compared to classical motor behavior tests. In SOD1 G93A, R6/2, and Q175KI mice, the alterations in gait were evident already several weeks earlier than with traditional gross motor assays. Kinematic testing can be applied to a wider set of motor readouts beyond gait in order to study whole body movement patterns such as with relation to joints and various body parts longitudinally, providing a sophisticated and translatable method for disseminating motor components in rodent disease models and evaluating therapeutic interventions.

Keywords: Gait analysis, kinematic, motor impairment, inherent feature

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Authors: Jyoti Agrawal

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Introduction: Renal diseases in children and young adult can be difficult to diagnose early as it may present only with few symptoms, tends to have different course than adult and respond variously to different treatment. The pattern of renal disease in children is different from developing countries as compared to developed countries. Methods: This study was a hospital based prospective observational study carried from March, 2014 to February 2015 at BP Koirala institute of health sciences. Patients with renal disease, both inpatient and outpatient from birth to 14 years of age were enrolled in the study. The diagnosis of renal disease was be made on clinical and laboratory criteria. Results: Total of 120 patients were enrolled in our study which contributed to 3.74% % of total admission. The commonest feature of presentation was edema (75%), followed by fever (65%), hypertension (60%), decreased urine output (45%) and hematuria (25%). Most common diagnosis was acute glomerulonephritis (40%) followed by Nephrotic syndrome (25%) and urinary tract infection (25%). Renal biopsy was done for 10% of cases and most of them were steroid dependent nephrotic syndrome. 5% of our cases expired because of multiorgan dysfunction syndrome, sepsis and acute kidney injury. Conclusion: Renal disease contributes to a large part of hospital pediatric admission as well as mortality and morbidity to the children.

Keywords: glomerulonephritis, nephrotic syndrome, renal disease, urinary tract infection

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Sara Sahraoui

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This research studied the effect of Alzheimer's disease (AD) on language information processing in subjects with Alzheimer’s disease (AD) who were bilingual (French and dialectical Arabic). The results show a disorder of certain semantic aspects of their mother tongue (L1). On the other hand, grammatical levels appeared to be relatively unaffected in oral speech in L1 but were disturbed in the second language (L2). In consequence, we constructed a cognitive-language stimulation protocol for bilingual patients (PSCLAB) to respond to this disorder. The efficacy of this protocol in terms of rehabilitation was assessed in 30 such patients through discourse analysis carried out before and after initiating the protocol. The results show that cognitive/language training using the PSCLAB appears to improve the language behaviour of bilingual patients with AD. However, this survey study aims to verify the satisfaction of patients’ relatives with the results of cognitive language training by PSCLAB. We developed a brief instrument to measure the satisfaction of family members. The results report that the patient's relatives are satisfied with the results of cognitive training by PSCLAB.

Keywords: satisfaction, Alzheimer's disease, rehabilitation, levels language

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Authors: Ali Bayram, Burak Uz, Remzi Yiğiter

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The impairment of lipid metabolism in the central nervous system has been suggested as a critical factor of Alzheimer’s disease (AD) pathogenesis. Homo sapiens acyl-coenyme A synthetase short-chain family member 2 (ACSS2) gene encodes the enzyme acetyl-Coenzyme A synthetase (AMP forming; AceCS) providing acetyl-coenzyme A (Ac-CoA) for various physiological processes, such as cholesterol and fatty acid synthesis, as well as the citric acid cycle. We investigated ACSS2, transcript variant 1 (ACSS2*1), mRNA levels in the peripheral blood mononuclear cells (PBMC) of patients with AD and compared them with the controls. The study group comprised 50 patients with the diagnosis of AD who have applied to Gaziantep University Faculty of Medicine, and Department of Neurology. 49 healthy individuals without any neurodegenerative disease are included as controls. ACSS2 mRNA expression in PBMC of AD/control patients was 0.495 (95% confidence interval: 0.410-0.598), p= .000000001902). Further studies are needed to better clarify this association.

Keywords: Alzheimer’s disease, ACSS2 Genes, mRNA expression, RT-PCR

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Authors: Ireneusz Majsterek, Dariusz Pytel, J. Alan Diehl

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PKR-like ER kinase (PERK) is serine/threonie endoplasmic reticulum (ER) transmembrane kinase activated during ER-stress. PERK can activate signaling pathways known as unfolded protein response (UPR). Attenuation of translation is mediated by PERK via phosphorylation of eukaryotic initiation factor 2α (eIF2α), which is necessary for translation initiation. PERK activation also directly contributes to activation of Nrf2 which regulates expression of anti-oxidant enzymes. An increased phosphorylation of eIF2α has been reported in Alzheimer disease (AD) patient hippocampus, indicating that PERK is activated in this disease. Recent data have revealed activation of PERK signaling in non-Hodgkins lymphomas. Results also revealed that loss of PERK limits mammary tumor cell growth in vitro and in vivo. Consistent with these observations, activation of UPR in vitro increases levels of the amyloid precursor protein (APP), the peptide from which beta-amyloid plaques (AB) fragments are derived. Finally, proteolytic processing of APP, including the cleavages that produce AB, largely occurs in the ER, and localization coincident with PERK activity. Thus, we expect that PERK-dependent signaling is critical for progression of many types of diseases (human cancer, neurodegenerative disease and other). Therefore, modulation of PERK activity may be a useful therapeutic target in the treatment of different diseases that fail to respond to traditional chemotherapeutic strategies, including Alzheimer’s disease. Our goal will be to developed therapeutic modalities targeting PERK activity.

Keywords: PERK kinase, small molecule inhibitor, neurodegenerative disease, Alzheimer’s disease

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Authors: Shohel Ahmed, Md. Abdul Alim, Sumaiya Rahman

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Optimal control can be helpful to test and compare different vaccination strategies of a certain disease. The mathematical model of HIV we consider here is a set of ordinary differential equations (ODEs) describing the interactions of CD4+T cells of the immune system with the human immunodeficiency virus (HIV). As an early treatment setting, we investigate an optimal chemotherapy strategy where control represents the percentage of effect the chemotherapy has on the system. The aim is to obtain a new optimal chemotherapeutic strategy where an isoperimetric constraint on the chemotherapy supply plays a crucial role. We outline the steps in formulating an optimal control problem, derive optimality conditions and demonstrate numerical results of an optimal control for the model. Numerical results illustrate how such a constraint alters the optimal vaccination schedule and its effect on cell-virus interactions.

Keywords: chemotherapy of HIV, optimal control involving ODEs, optimality conditions, Pontryagin’s maximum principle

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Authors: Djamila Meghraoui, Bachir Boudraa, Thouraya Meksen, M.Boudraa

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Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.

Keywords: Parkinson’s disease recognition, PCA, MDVP, multinomial Naive Bayes

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Authors: Mehdi Nasr Esfahani, Abdal-Rasool Gholamalian, Abdelfattah A. Dababat

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Wheat, Triticum aestivum L. is one of the most important and strategic crops in the human diet. Several diseases threaten this particular crop. Common root rot disease of wheat by a fungal agent, Bipolaris sorokiniana is one of the important diseases, causing considerable losses worldwide. Resistant sources are the only feasible and effective method of control for managing diseases. In this study, the response of 33 domestic and exotic wheat genotypes, including cultivars and promising lines were screened to B. sorokiniana at greenhouse and field conditions, based on five scoring scale indexes of 0 to 100 severity percentage. The screening was continued on resistant wheat genotypes and repeated several times to confirm the greenhouse and field results. Statistical and cluster analysis of data was performed using SAS and SPSS software, respectively. The results showed that, the response of wheat genotypes to the disease in the greenhouse and field conditions was highly significant. The highest rate of common root rot disease infection, B. sorokiniana in the greenhouse and field, was of CVS. Karkheh and Beck Cross-Roshan with 60.83% and 59.16% disease severity respectively, and the lowest one were in cv. Alvand with 18.33%, followed by cv. Baharan with 19.16% disease severity, with a highly significant difference respectively. The remaining wheat genotypes were located in between these two highest and lowest infected groups to B. sorokiniana significantly. There was a high correlation coefficient between the related statistical groups and cluster analysis.

Keywords: wheat, rot, root, crown, fungus, genotype, resistance

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Authors: Arghavan Tonkaboni, Shamsolmolouk Najafi, Mohmmad Taghi Kiani, Mehrzad Gholampour, Touraj Goli

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Introduction: Recurrent aphthous stomatitis (RAS) is a multifactorial recurrent oral lesion; which is an autoimmune disease. TH1 cytokines are the most important etiological factors. Autoimmune thyroid disease (ATD) is one of the most common autoimmune diseases and generally coexists with other autoimmune diseases. This study assessed the prevalence of thyroid disease in patients with recurrent aphthous stomatitis. Materials and Methods: This case control study assessed 100 known RAS patients who were diagnosed clinically by oral medicine specialists; venous blood samples were analyzed for thyroid stimulating hormone (TSH), free triiodothyronine (fT3), total thyroxine (fT4), thyroglobulin, anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-TG) levels. Results: Fifty patients with RAS aged between 18-42 years (28.5±5.8) and 50 healthy volunteers aged 19-45 years (27.3±5.4) participated. In RAS patients, fT3 and TSH levels were significantly higher (P=0.031, P=0.706); however, fT4 level was lower in the RAS group (P=0.447). Anti TG and anti-TPO levels were significantly higher in the RAS group (P=0.008, P=0.067). Conclusion: Our study showed that ATD prevalence was significantly higher in RAS patients. Based on this study, we recommend assessment of thyroid hormones and antibodies in RAS patients.

Keywords: recurrent aphthous stomatitis, thyroid antibodies, thyroid hormone, thyroid autoimmune disease

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