Search results for: chronic disease.

Authors: Md R. Bashar, Yan Li, Peng Wen

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This study is to investigate the electroencephalogram (EEG) differences generated from a normal and Alzheimer-s disease (AD) sources. We also investigate the effects of brain tissue distortions due to AD on EEG. We develop a realistic head model from T1 weighted magnetic resonance imaging (MRI) using finite element method (FEM) for normal source (somatosensory cortex (SC) in parietal lobe) and AD sources (right amygdala (RA) and left amygdala (LA) in medial temporal lobe). Then, we compare the AD sourced EEGs to the SC sourced EEG for studying the nature of potential changes due to sources and 5% to 20% brain tissue distortions. We find an average of 0.15 magnification errors produced by AD sourced EEGs. Different brain tissue distortion models also generate the maximum 0.07 magnification. EEGs obtained from AD sources and different brain tissue distortion levels vary scalp potentials from normal source, and the electrodes residing in parietal and temporal lobes are more sensitive than other electrodes for AD sourced EEG.

Keywords: Alzheimer's disease (AD), brain tissue distortion, electroencephalogram, finite element method.

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Authors: S. K. Aruna, S. Chitra

Abstract:

This research paper presents a framework for classifying Magnetic Resonance Imaging (MRI) images for Dementia. Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures. Characterizing morphological changes helps understand disease development and contributes to early prediction and prevention of the disease. Modelling, that captures the brain’s structural variability and which is valid in disease classification and interpretation is very challenging. Features are extracted using Gabor filter with 0, 30, 60, 90 orientations and Gray Level Co-occurrence Matrix (GLCM). It is proposed to normalize and fuse the features. Independent Component Analysis (ICA) selects features. Support Vector Machine (SVM) classifier with different kernels is evaluated, for efficiency to classify dementia. This study evaluates the presented framework using MRI images from OASIS dataset for identifying dementia. Results showed that the proposed feature fusion classifier achieves higher classification accuracy.

Keywords: Magnetic resonance imaging, dementia, Gabor filter, gray level co-occurrence matrix, support vector machine.

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Authors: E. N. Hasanov, K. Y. Yusifova, M. A. Ali

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Rabies is a zoonotic disease that causes acute encephalitis in domestic and wild carnivores. The goal of this investigation was to analyze the data on oral vaccination of wild canids and stray dogs in Azerbaijan. Before the start of vaccination campaign conducted by the IDEA (International Dialogue for Environmental Action) Animal Care Center (IACC), all rabies cases in Azerbaijan for the period of 2017-2020 were analyzed. So, 30 regions for oral immunization with the Rabadrop vaccine were selected. In total, 95.9 thousand doses of baits were scattered in 30 regions, 970 (0.97%) remained intact. In addition, a campaign to sterilize and vaccinate stray dogs and cats undoubtedly had a positive impact on reducing the dynamics of rabies incidence. During the period 2017-2020, 2,339 dogs and 2,962 cats were sterilized and vaccinated under this program. It can be noted that the risk of rabies infection can be reduced through special preventive measures against disease reservoirs, which include oral immunization of wild and stray animals.

Keywords: Rabies, vaccination, oral immunization, wild canids, stray dogs, vaccine, disease reservoirs.

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Authors: R. Gaceb-Terrak, F. Rahmania

Abstract:

Takerbucht is the only cultivar of date palm known as being resistant to the bayoud disease, caused by Fusarium oxysporum f. sp. albedinis (F.o.a.). In the aim to understand more about the defense mechanisms implied, we realized phytochemical analyses of this cultivar leaflets and roots and this, for the first time, using gas chromatography-mass spectrometry (GC-MS).The examination of our results shows that fifty-four molecules have been detected, fourteen of which are common to leaflets and roots. This study revealed also the organs' richness in derivatives fatty acids: both saturated and unsaturated are represented mainly by methyl esters of Hexadecanoic and 9,12,15-Octadecatrienoic acids. 1-Dodecanethiol, derivative Dodecanoic acid is only present in roots. It’s of great interest to note that the screening revealed the steroidal saponins abundance, among which Yamogenin acetate and Diosgenin, exclusively detected in Takerbucht. They may play an essential role, in the date palm resistance to the bayoud disease.

Keywords: Analysis by GC-MS, leaflets and roots of resistant date palm to F.o.a., derivatives fatty acids, steroidal saponins.

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Authors: Einalou Z., Najafi Z., Maghooli K. Zandi Y, Sheibeigi A

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In this study, an investigation over digestive diseases has been done in which the sound acts as a detector medium. Pursue to the preprocessing the extracted signal in cepstrum domain is registered. After classification of digestive diseases, the system selects random samples based on their features and generates the interest nonstationary, long-term signals via inverse transform in cepstral domain which is presented in digital and sonic form as the output. This structure is updatable or on the other word, by receiving a new signal the corresponding disease classification is updated in the feature domain.

Keywords: Cepstrum, databank, digestive disease, acousticsignal.

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Authors: Cornelia-Eugenia Munteanu

Abstract:

The goal of this paper is to present the diagnostic contribution that the screening instrument, Mini-Mental State Examination-2: Expanded Version (MMSE-2:EV), brings in detecting the cognitive impairment or in monitoring the progress of degenerative disorders. The diagnostic signification is underlined by the interpretation of the MMSE-2:EV scores, resulted from the test application to patients with mild and major neurocognitive disorders. The cases were selected from current practice, in order to cover vast and significant neurocognitive pathology: mild cognitive impairment, Alzheimer’s disease, vascular dementia, mixed dementia, Parkinson’s disease, conversion of the mild cognitive impairment into Alzheimer’s disease. The MMSE-2:EV version was used: it was applied one month after the initial assessment, three months after the first reevaluation and then every six months, alternating the blue and red forms. Correlated with age and educational level, the raw scores were converted in T scores and then, with the mean and the standard deviation, the z scores were calculated. The differences of raw scores between the evaluations were analyzed from the point of view of statistic signification, in order to establish the progression in time of the disease. The results indicated that the psycho-diagnostic approach for the evaluation of the cognitive impairment with MMSE-2:EV is safe and the application interval is optimal. In clinical settings with a large flux of patients, the application of the MMSE-2:EV is a safe and fast psychodiagnostic solution. The clinicians can draw objective decisions and for the patients: it does not take too much time and energy, it does not bother them and it doesn’t force them to travel frequently.

Keywords: MMSE-2, dementia, cognitive impairment, neuropsychology.

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Authors: Steady Mushayabasa

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Typhoid fever is a communicable disease, found only in man and occurs due to systemic infection mainly by Salmonella typhi organism. The disease is endemic in many developing countries and remains a substantial public health problem despite recent progress in water and sanitation coverage. Globally, it is estimated that typhoid causes over 16 million cases of illness each year, resulting in over 600,000 deaths. A mathematical model for assessing the impact of educational campaigns on controlling the transmission dynamics of typhoid in the community, has been formulated and analyzed. The reproductive number has been computed. Stability of the model steady-states has been examined. The impact of educational campaigns on controlling the transmission dynamics of typhoid has been discussed through the basic reproductive number and numerical simulations. At its best the study suggests that targeted education campaigns, which are effective at stopping transmission of typhoid more than 40% of the time, will be highly effective at controlling the disease in the community. 

Keywords: Mathematical model, Typhoid, saturated incidence rate, treatment, reproductive number, sensitivity analysis.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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Authors: Randa M. Ali, Naguiba F. Loutfy, Osama M. Awad

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Leishmaniasis is the collective name for a number of diseases caused by protozoan flagellates of the genus Leishmania, which is transmitted by Phlebotomine sandfly, the disease has diverse clinical manifestations and found in many areas of the world, particularly in Africa, Latin America, South and Central Asia, the Mediterranean basin and the Middle East. This study was done to assess primary health care physicians’ knowledge (PHP) and attitude about leishmaniasis and to assess awareness of local inhabitants about the disease and its vector in four areas in west Alexandria, Egypt. It is a cross sectional survey that was conducted in four PHC units in west Alexandria. All physicians currently working in these units during the study period were invited to participate in the study; only 20 PHP completed the questionnaire. 60 local inhabitants were selected randomly from the four areas of the study, 15 from each area; Data was collected through two different specially designed questionnaires. Results showed that 11 (55%) percent of the physicians had satisfactory knowledge; they answered more than 9 (60%) questions out of a total 14 questions about leishmaniasis and sandfly. On the other hand when attitude of the primary health care physicians about leishmaniasis was measured, results showed that 17 (85%) had good attitude and 3 (15%) had poor attitude. The second questionnaire showed that the awareness of local inhabitants about leishmaniasis and sandfly as a vector of the disease is poor and needs to be corrected. (90%) of the interviewed inhabitants had not heard about leishmaniasis, Only 3 (5%) of them said they know sandfly and its role in transmission of leishmaniasis. Thus we conclude that knowledge and attitudes of physicians are acceptable. However, there is, room for improvement and could be done through formal training courses and distribution of guidelines. In addition to raising the awareness of primary health care physicians about the importance of early detection and notification of cases of leishmaniasis, health education for raising awareness of the public regarding the vector and the disease is necessary because related studies have demonstrated that for inhabitants to take enough protective measures against the vector, they should perceive that it is responsible for causing a disease.

Keywords: Attitude, knowledge, PHP, leishmaniasis, sandfly, local inhabitants, inside and outside housing conditions.

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

Abstract:

A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme Gradient Boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impairment, multiclass classification, ADNI, support vector machine, random forest.

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

Abstract:

The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: lexical semantics, feature representation, semantic decision, convolutional neural network, electronic medical record

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Authors: Inas E. Darwish, Alia M. Arab, Tarek A. Azeim, Teshreen M. Zeitoun, Wafaa A. Hewedy, Moemen A. Heiba, Iman S. Emara

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Inflammatory bowel disease (IBD) is a chronic relapsing-remitting condition that afflicts millions of people throughout the world and impairs their daily functions and quality of life. Treatment of IBD depends largely on 5-aminosalicylic acid (5- ASA) and corticosteroids. The present study aimed to clarify the effects of 5-aminosalicylic acid, budesonide and currcumin on 90 male albino rats against trinitrobenzene sulfonic acid (TNB) induced colitis. TNB was injected intrarectally to 50 rats. The other 40 rats served as control groups. Both 5-ASA (in a dose of 120 mg/kg) and budesonide (in a dose of 0.1 mg/kg) were administered daily for one week whereas currcumin was injected intraperitonially (in a dose of 30 mg/kg daily) for 14 days after injection of either TNB in the colitis rats (group B) or saline in control groups (group A). The study included estimation of macroscopic score index, histological examination of H&E stained sections of the colonic tissue, biochemical estimation of myeloperoxidase (MPO), nitric oxide (NO), and caspase-3 levels, in addition to studying the effect of tested drugs on colonic motility. It was found that budesonide and curcumin improved mucosal healing, reduced both NO production and caspase- 3 level. They had the best impact on the disturbed colonic motility in TNBS-model of colitis.

Keywords: Colitis, curcumin, nitric oxide.

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Authors: Dennys Robson Girardi, Hugo Bulegon, Claudia Maria Moro Barra

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Leprosy is an infectious disease caused by Mycobacterium Leprae, this disease, generally, compromises the neural fibers, leading to the development of disability. Disabilities are changes that limit daily activities or social life of a normal individual. When comes to leprosy, the study of disability considered the functional limitation (physical disabilities), the limitation of activity and social participation, which are measured respectively by the scales: EHF, SALSA and PARTICIPATION SCALE. The objective of this work is to propose an on-line monitoring of leprosy patients, which is based on information scales EHF, SALSA and PARTICIPATION SCALE. It is expected that the proposed system is applied in monitoring the patient during treatment and after healing therapy of the disease. The correlations that the system is between the scales create a variety of information, presented the state of the patient and full of changes or reductions in disability. The system provides reports with information from each of the scales and the relationships that exist between them. This way, health professionals, with access to patient information, can intervene with techniques for the Prevention of Disability. Through the automated scale, the system shows the level of the patient and allows the patient, or the responsible, to take a preventive measure. With an online system, it is possible take the assessments and monitor patients from anywhere.

Keywords: Leprosy, Medical Informatics, Decision SupportSystem, Disability.

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Authors: Ibrahim M. S. Shnawa, Bashar A. H. E. Alsadi, Kalida K. Alniaem

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In the immunologic sense, clinical infection is a state of failure of the immune system to combat the pathogenic weapon of the bacteria invading the host. A motile gram negative vibroid organism associated with marked mono and poly nuclear cell responses was traced during the examination of a clinical material from an infected common carp Cyprinus carpio. On primary plate culture, growth was shown to be pure, dense population of an Aeromonas-like colony morphotype. The pure isolate was found to be; Aerobic, facultatively anaerobic, non-halophilic, grew at 0C, and 37C, oxidase positive utilizes glucose through fermentative pathway, resist 0/129 and novobiocin, produces alanine and lysine decarboxylases but non-producing ornithine dehydrolases. Tests for the in vitro determinants of pathogenicity has shown to be; Betahaemolytic onto blood agar, gelatinase, casienase and amylase producer. Three in vivo determinants of pathogenicity were tested as, the lethal dose fifty, the pathogenesis and pathogenicity. It was evident that 0.1 milliliter of the causal bacterial cell suspension of a density 1 x 107 CFU/ml injected intramuscularly into an average of 100gms fish toke five days incubation period, then at the day six morbidity and mortality were initiated. LD50 was recorded at the day 12 post-infection. Use of an LD50 doses to study the pathogenicity, reveals mononuclear and polynuclear cell responses, on examining the stained direct films of the clinical materials from the experimentally infected fish. Re-isolation tests confirm that the reisolant is same. The course of the infection in natural case was shown manifestation of; skin ulceration, haemorrhage and descaling. On evisceration, the internal organs were shown; congestion in the intestines, spleen and, air sacs. The induced infection showed a milder form of these manifestations. The grading of the virulence of this organism was virulent causing chronic course of infections as indicated from the pathogenesis and pathogenicity studies. Thus the infectious bacteria were consistent with Aeromonas hydrophila, and the infection was chronic.

Keywords: Piscan, inflammatory respnonse, pure culture, pathogen, chronic, infection.

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Authors: Syed Dawood Md. Taimur, Md. Hasanur Rahman, Syeda Fahmida Afrin, Farzana Islam

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The concept of myocardial injury, although first recognized from animal studies, is now recognized as a clinical phenomenon that may result in microvascular damage, no-reflow phenomenon, myocardial stunning, myocardial hibernation and ischemic preconditioning. The final consequence of this event is left ventricular (LV) systolic dysfunction leading to increased morbidity and mortality. The typical clinical case of reperfusion injury occurs in acute myocardial infarction (MI) with ST segment elevation in which an occlusion of a major epicardial coronary artery is followed by recanalization of the artery. This may occur spontaneously or by means of thrombolysis and/or by primary percutaneous coronary intervention (PCI) with efficient platelet inhibition by aspirin (acetylsalicylic acid), clopidogrel and glycoprotein IIb/IIIa inhibitors. In recent years, percutaneous coronary intervention (PCI) has become a well-established technique for the treatment of coronary artery disease. PCI improves symptoms in patients with coronary artery disease and it has been increasing safety of procedures. However, peri- and post-procedural myocardial injury, including angiographical slow coronary flow, microvascular embolization, and elevated levels of cardiac enzyme, such as creatine kinase and troponin-T and -I, has also been reported even in elective cases. Furthermore, myocardial reperfusion injury at the beginning of myocardial reperfusion, which causes tissue damage and cardiac dysfunction, may occur in cases of acute coronary syndrome. Because patients with myocardial injury is related to larger myocardial infarction and have a worse long-term prognosis than those without myocardial injury, it is important to prevent myocardial injury during and/or after PCI in patients with coronary artery disease. To date, many studies have demonstrated that adjunctive pharmacological treatment suppresses myocardial injury and increases coronary blood flow during PCI procedures. In this review, we highlight the usefulness of pharmacological treatment in combination with PCI in attenuating myocardial injury in patients with coronary artery disease.

Keywords: Coronary artery disease, Percutaneous coronary intervention, Myocardial injury, Pharmacology.

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Authors: Dwitya Elvira, Eryati Darwin

Abstract:

Background: Graves’ disease (GD) is an autoimmune thyroid disease. Imbalance of Th1/Th2 cells and T-regulatory (Treg)/Th17 cells was thought to play pivotal role in the pathogenesis of GD. Treg FoxP3 produced TGF-β to maintain regulatory function, and Th17 cells produced IL-17 as cytokines that were thought in mediating several autoimmune diseases. The aim of this study is to assess the role of IL-17 and TGF-β in the pathogenesis of GD and to investigate its correlation with Thyroid Stimulating Hormone Receptor Antibody (TRAb) and Treg FoxP3 expression. Method: 30 GD patients and 27 age and sex-matched controls were enrolled in this study. Diagnosis of GD was based on clinical and biochemical of GD. Serum IL-17, TGF-β, TRAb, and FoxP3 were measured by enzyme-linked immunosorbent assay (ELISA). Data were analyzed by using SPSS 21.0 (SPSS Inc.). Spearman rank correlation test was used for assessment of correlation. The statistical significance was accepted as P<0.05. Result: There was no significant correlation between IL-17 and TGF-β serum with expression of FoxP3 level in GD, but there was significant correlation between TGF-β and TRAb serum level (P<0.05). Serum levels of IL-17 and TGF-β were found to be elevated in patient group compared to control, where mean values of IL-17 were 14.43±2.15 pg/mL and TGF-β were 10.44±3.19 pg/mL in patients group; and in control group, level of IL-17 were 7.1±1.45 pg/mL and TGF-β were 4.95±1.35 pg/mL. Conclusion: Serum Il-17 and TGF-β were elevated in GD patients that reflect the role of inflammatory and regulatory cytokines activation in pathogenesis of GD. There was significant correlation between TGF-β and TRAb, revealing that Treg cytokines may play a role in pathogenesis of GD.

Keywords: IL-17, TGF-β, FoxP3, Graves’ disease.

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Authors: Huong M. T. Nguyen, Hoa A. P. Nguyen, Mai P. T. Nguyen, Ngoc D. Ngo, Van T. Ta, Hai T. Le, Chi V. Phan

Abstract:

Wilson’s disease (WD) is an autosomal recessive disorder of the copper metabolism, which is caused by a mutation in the copper-transporting P-type ATPase (ATP7B). The mechanism of this disease is the failure of hepatic excretion of copper to bile, and leads to copper deposits in the liver and other organs. The ATP7B gene is located on the long arm of chromosome 13 (13q14.3). This study aimed to investigate the gene mutation in the Vietnamese patients with WD, and make a presymptomatic diagnosis for their familial members. Forty-three WD patients and their 65 siblings were identified as having ATP7B gene mutations. Genomic DNA was extracted from peripheral blood samples; 21 exons and exon-intron boundaries of the ATP7B gene were analyzed by direct sequencing. We recognized four mutations ([R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G) in the sum of 20 detectable mutations, accounting for 87.2% of the total. Mutation S105* was determined to have a high rate (32.6%) in this study. The hotspot regions of ATP7B were found at exons 2, 16, and 8, and intron 14, in 39.6 %, 11.6 %, 9.3%, and 7 % of patients, respectively. Among nine homozygote/compound heterozygote siblings of the patients with WD, three individuals were determined as asymptomatic by screening mutations of the probands. They would begin treatment after diagnosis. In conclusion, 20 different mutations were detected in 43 WD patients. Of this number, four novel mutations were explored, including [R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G. The mutation S105* is the most prevalent and has been considered as a biomarker that can be used in a rapid detection assay for diagnosis of WD patients. Exons 2, 8, and 16, and intron 14 should be screened initially for WD patients in Vietnam. Based on risk profile for WD, genetic testing for presymptomatic patients is also useful in diagnosis and treatment.

Keywords: ATP7B gene, mutation detection, presymptomatic diagnosis, Vietnamese Wilson’s disease.

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Authors: G. Tamilpavai, C. Vishnuppriya

Abstract:

Studying DNA (deoxyribonucleic acid) sequence is useful in biological processes and it is applied in the fields such as diagnostic and forensic research. DNA is the hereditary information in human and almost all other organisms. It is passed to their generations. Earlier stage detection of defective DNA sequence may lead to many developments in the field of Bioinformatics. Nowadays various tedious techniques are used to identify defective DNA. The proposed work is to analyze and identify the cancer-causing DNA motif in a given sequence. Initially the human DNA sequence is separated as k-mers using k-mer separation rule. The separated k-mers are clustered using Self Organizing Map (SOM). Using Levenshtein distance measure, cancer associated DNA motif is identified from the k-mer clusters. Experimental results of this work indicate the presence or absence of cancer causing DNA motif. If the cancer associated DNA motif is found in DNA, it is declared as the cancer disease causing DNA sequence. Otherwise the input human DNA is declared as normal sequence. Finally, elapsed time is calculated for finding the presence of cancer causing DNA motif using clustering formation. It is compared with normal process of finding cancer causing DNA motif. Locating cancer associated motif is easier in cluster formation process than the other one. The proposed work will be an initiative aid for finding genetic disease related research.

Keywords: Bioinformatics, cancer motif, DNA, k-mers, Levenshtein distance, SOM.

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Authors: Farhan Ismaeel Dakheel, Raoof Smko, K. Negrat, Abdelsalam Almarimi

Abstract:

In this paper we used data mining techniques to identify outlier patients who are using large amount of drugs over a long period of time. Any healthcare or health insurance system should deal with the quantities of drugs utilized by chronic diseases patients. In Kingdom of Bahrain, about 20% of health budget is spent on medications. For the managers of healthcare systems, there is no enough information about the ways of drug utilization by chronic diseases patients, is there any misuse or is there outliers patients. In this work, which has been done in cooperation with information department in the Bahrain Defence Force hospital; we select the data for Cardiac patients in the period starting from 1/1/2008 to December 31/12/2008 to be the data for the model in this paper. We used three techniques for finding the drug utilization for cardiac patients. First we applied a clustering technique, followed by measuring of clustering validity, and finally we applied a decision tree as classification algorithm. The clustering results is divided into three clusters according to the drug utilization, for 1603 patients, who received 15,806 prescriptions during this period can be partitioned into three groups, where 23 patients (2.59%) who received 1316 prescriptions (8.32%) are classified to be outliers. The classification algorithm shows that the use of average drug utilization and the age, and the gender of the patient can be considered to be the main predictive factors in the induced model.

Keywords: Data Mining, Clustering, Classification, Drug Utilization..

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Authors: N. H. Harun, A. S. Abdul Nasir, M. Y. Mashor, R. Hassan

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Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year. There are two main categories for leukaemia, which are acute and chronic leukaemia. The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image. In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively. Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving kmeans clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia.

Keywords: Acute Leukaemia Images, Clustering Algorithms, Image Segmentation, Moving k-Means.

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

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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.

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Authors: Muhibul H. Bhuyan, Mafujul Hasan

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If a person can monitor his/her heart rate regularly then he/she can detect heart disease early and thus he/she can enjoy longer life span. Therefore, this disease should be taken seriously. Hence, many health care devices and monitoring systems are being designed to keep track of the heart disease. This work reports a design and simulation processes of an Arduino microcontroller based heart rate measurement and monitoring system in Proteus environment. Clipping sensors were utilized to sense the heart rate of an individual from the finger tips. It is a digital device and uses mainly infrared (IR) transmitter (mainly IR LED) and receiver (mainly IR photo-transistor or IR photo-detector). When the heart pumps the blood and circulates it among the blood vessels of the body, the changed blood pressure is detected by the transmitter and then reflected back to the receiver accordingly. The reflected signals are then processed inside the microcontroller through a software written assembly language and appropriate heart rate (HR) is determined by it in beats per minute (bpm) from the detected signal for a duration of 10 seconds and display the same in bpm on the LCD screen in digital format. The designed system was simulated on several persons with varying ages, for example, infants, adult persons and active athletes. Simulation results were found very satisfactory.

Keywords: Heart rate measurement, design, simulation, Proteus, Arduino Uno microcontroller.

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Authors: Abeer Aljohani

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The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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Authors: L. Nekoozad, M. Salehi Barough, B. Salmasian

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Introduction: Some parameters should be considered to investigate the chronic effects of radiation absorption in radiation workers. Trace elements are parameters which small changes in them can cause significant effects on live systems. The role of trace element concentration in human health is significant. These elements play an important role in the developing and functioning of the immune system, cellular respiration, and oxidation processes. Considering the importance and necessity of this issue and few studies, measurements of concentration changes of these elements due to the absorbed dose are important. Purpose: This study aimed to determine the biological effects of occupational dose absorption on plasma and erythrocyte concentration of Zn and Cu in the radiology staff of Tehran Oil Hospital. Material and methods: In this analytical-comparative study, 72 people have entered. 36 people (18 males and 18 females) were selected as radiology staff in the diagnostic and therapeutic departments of Tehran Oil Hospital. And 36 people (18 males and 18 females) were selected as general section staff in the same hospital as a control group. Radiology and control groups’ age and sex were matched. 10 ml of venous blood was taken from all people.  An atomic absorption spectrometer was used to obtain zinc and copper plasma concentrations. Levine test was used to compare these results validity. Results: The mean concentrations of copper and zinc were measured as 0.951 and 0.754 mg/L in the plasma phase and 3.2  and 0.401 mg/L in the RBC phase for the radiology group.  Copper and zinc average concentrations, respectively 0.976 and 0.813 mg/L in the plasma phase and 2.906 and 0.476 mg/L in the RBC phase, were measured for the control group. These elements Concentrations in the plasma phase were significantly different from that of the control group, but the concentrations in the red blood cell phase did not show a significant difference compared to the control group. In comparison, a separate comparison between men and women in the experimental and control groups showed a significant difference in the values of the elements mentioned. With a significant increase in samples, a better justification than the available statistical results can be extracted. Conclusions: Within this study results, chronic occupational probabilistic absorption destructive effects (even within the permitted range) on blood trace element concentration have been confirmed.

Keywords: Chronic absorption, atomic absorption spectrometry, radiology staff, trace element concentration.

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Authors: P. Pongsumpun

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The Swine flu outbreak in humans is due to a new strain of influenza A virus subtype H1N1 that derives in part from human influenza, avian influenza, and two separated strains of swine influenza. It can be transmitted from human to human. A mathematical model for the transmission of Swine flu is developed in which the human populations are divided into two classes, the risk and non-risk human classes. Each class is separated into susceptible, exposed, infectious, quarantine and recovered sub-classes. In this paper, we formulate the dynamical model of Swine flu transmission and the repetitive contacts between the people are also considered. We analyze the behavior for the transmission of this disease. The Threshold condition of this disease is found and numerical results are shown to confirm our theoretical predictions.

Keywords: Mathematical model, Steady state, Swine flu, threshold condition.

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Authors: Adekunle O. Afolabi, Pekka Toivanen

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The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.

Keywords: Recommendation systems, healthcare, internet of things, real-time, homecare.

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Authors: Amani Ashari, Julia Omar, Arif Hashim, Shahrul Hamid

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Apolipoprotein E (APOE) gene polymorphism has influence on serum lipids which relates to cardiovascular risk. The purpose of this study was to determine the frequency distribution of APOE alleles among Malaysian Type 2 Diabetes Mellitus (DM) patients with and without coronary artery disease (CAD) and their association with serum lipid profiles. A total of 115 patients were recruited in which 78 patients had Type 2 DM without CAD and 37 patients had Type 2 DM with CAD. The APOE polymorphism was detected by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The APOE ɛ3 allele was the most common one in both groups. There was no significant association between the APOE genotypes and the CAD status in Type 2 DM using Pearson χ² test. Further analysis indicated there were no significant differences in all lipid parameters between E2, E3 and E4 subgroups in both groups. The study showed that the E4 allele carriers of Type 2 DM with CAD patients had higher LDL-C level and lower HDL-C level compared to the other allele carriers. However, analyses showed these levels were not statistically different. The study also showed that the Type 2 DM with CAD group with E2 allele had higher triglyceride (TG). In conclusion, further study with larger sample size is needed to confirm role of E4 as a marker of CAD among Type 2 DM patients in Malaysian population.

Keywords: Apolipoprotein E, diabetes mellitus, cardiovascular disease, lipids.

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Authors: Mohsen Shirani, Micaela Demichela

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During the last decades, a number of food crises such as Bovine Spongiform Encephalopathy (BSE), Mad-Cow disease, Dioxin in chicken food, Food-and-Mouth Disease (FMD), have certainly inflected the reliability of the food industry. Consequently, the trend in applying different scientific methods of risk assessment in food safety has obtained more attentions in the academic and practice. However, lack of practical approach considering entire food supply chain is tangible in the academic literature. In this regard, this paper aims to apply risk assessment tool (FMEA) with integration of Human Factor along the entire supply chain of food production and test the method in a case study of Diary production, and analyze its results.

Keywords: Food Risk Assessment, FMEA, Human Factor.

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Authors: Lindsay Fink, Sarha Smith-Moyo, Leanne W. Charlesworth

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In recent years, the number of individuals experiencing homelessness has increased in the United States. This paper analyzes 2019 data from 16 different emergency shelters in Monroe County, located in Upstate New York. The data were collected through the County’s Homeless Management Information System (HMIS), and individuals were de-identified and de-duplicated for analysis. The purpose of this study is to explore the basic characteristics of the homeless population in Monroe County, and the dynamics of shelter use. The results of this study showed gender as a significant factor when analyzing the relationship between demographic variables and recorded reasons for shelter entry. Results also indicated that age and ethnicity did not significantly influence odds of re-entering a shelter, but did significantly influence reasons for shelter entry. Overall, the most common recorded cause of shelter entry in 2019 in the examined county was eviction by primary tenant. Recommendations to better address recurrent shelter entry and potential chronic homelessness include more consideration for the diversity existing within the homeless population, and the dynamics leading to shelter stays, including enhanced funding and training for shelter staff, as well as expanded access to permanent supportive housing programs.

Keywords: Chronic homelessness, homeless shelter stays, permanent supportive housing, shelter population dynamics.

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Authors: Climstein Mike, Walsh Joe, John Best, Heazlewood Ian Timothy, Burke Stephen, Kettunen Jyrki, Adams Kent, DeBeliso Mark

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Recently, the health of retired National Football League players, particularly lineman has been investigated. A number of studies have reported increased cardiometabolic risk, premature ardiovascular disease and incidence of type 2 diabetes. Rugby union players have somatotypes very similar to National Football league players which suggest that rugby players may have similar health risks. The International Golden Oldies World Rugby Festival (GORF) provided a unique opportunity to investigate the demographics of veteran rugby players. METHODOLOGIES: A cross-sectional, observational study was completed using an online web-based questionnaire that consisted of medical history and physiological measures. Data analysis was completed using a one sample t-test (<50yrs versus >50yrs) and Chi-square test. RESULTS: A total of 216 veteran rugby competitors (response rate = 6.8%) representing 10 countries, aged 35-72 yrs (mean 51.2, S.D. ±8.0), participated in the online survey. As a group, the incidence of current smokers was low at 8.8% (avg 72.4 cigs/wk) whilst the percentage consuming alcohol was high (93.1% (avg 11.2 drinks/wk). Competitors reported the following top six chronic diseases/disorders; hypertension (18.6%), arthritis (OA/RA, 11.5%), asthma (9.3%), hyperlipidemia (8.2%), diabetes (all types, 7.5%) and gout (6%), there were significant differences between groups with regard to cancer (all types) and migraines. When compared to the Australian general population (Australian Bureau of Statistics data, n=18,000), GORF competitors had a Climstein Mike, Walsh Joe (corresponding author) and Burke Stephen School of Exercise Science, Australian Catholic University, 25A Barker Road, Strathfield, Sydney, NSW, 2016, Australia (e-mail: [email protected], [email protected], [email protected]). John Best is with Orthosports, 160 Belmore Rd., Randwick, Sydney,NSW 2031, Australia (e-mail: [email protected]). Heazlewood, Ian Timothy is with School of Environmental and Life Sciences, Faculty Education, Health and Science, Charles Darwin University, Precinct Yellow Building 2, Charles Darwin University, NT 0909, Australia (e-mail: [email protected]). Kettunen Jyrki Arcada University of Applied Sciences, Jan-Magnus Janssonin aukio 1, FI-00550, Helsinki, Finland (e-mail: [email protected]). Adams Kent is with California State University Monterey Bay, Kinesiology Department, 100 Campus Center, Seaside, CA., 93955, USA (email: [email protected]). DeBeliso Mark is with Department of Physical Education and Human Performance, Southern Utah University, 351 West University Blvd, Cedar City, Utah, USA (e-mail: [email protected]). significantly lower incidence of anxiety (p<0.01), arthritis (p<0.06), depression (p<.01) however, a significantly higher incidence of diabetes (p<0.03) and hypertension (p<0.01). The GORF competitors also reported taking the following prescribed medications; antihypertensive (13%), hypolipidemics (8%), non-steroidal anti-inflammatory (6%), and anticoagulants (4%). Significant differences between groups were observed in antihypertensives, anticoagulants and hypolipidemics. There were significant (p<0.05) differences between groups (<50yrs versus >50yrs) with regard to height (180 vs 177cm), weight (97.6 vs 93.1Kg-s), BMI (30 vs 29.7kg/m2) and waist circumference (85.7 vs 93.1cm) however, there were no differences in subsequent parameters of systolic blood pressure, diastolic blood pressure, total cholesterol, HDL-C, LDL-C, triglycerides-C or fasting plasma glucose. CONCLUSIONS: This represents the first collection of demographics on this cohort. GORF participants demonstrated increased cardiometabolic risk with regard to the incidence of hypercholesterolemia, hypertension and type 2 diabetes. Preventative strategies should be developed to reduce this risk with education of these risks for future participants.

Keywords: Masters athlete, rugby union, risk factors, chronic disease.

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