Search results for: mortality.

Authors: N. B. Dhayanithi, T. T. Ajith Kumar, T. Balasubramanian

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Present study summarizes the control of Vibrio alginolyticus infection in hatchery reared Clownfish, Amphiprion sebae with the extract of the mangrove plant, Avicennia marina. Fishes with visible symptoms of hemorrhagic spots were chosen and the genomic DNA of the causative bacterium was isolated and sequenced based on 16S rDNA gene. The in vitro assay revealed that a fraction of A. marina leaf extract elucidated with ethyl acetate: methanol (6:4) showed a high activity (28 mm) at 125 μg/ml concentrations. About 4 % of the fraction fed along with live V. alginolyticus was significantly decreased the cumulative mortality (P<0.05) in the experimental groups than the control group. The responsible fraction was investigated by gas chromatography - mass spectroscopy and found the presence of active compounds. This is the first research in India to control vibriosis infection in marine ornamental fish with mangrove leaf extract.

Keywords: Amphiprion seabe, Avicennia marina, Gas Chromatography - Mass Spectroscopy, Vibrio alginolyticus

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Authors: Isaura Esther Solano Núñez, David Suarez

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The main purpose of this research is to discover what causes death in children of the Wayuu community, and deeply analyze those results in order to take corrective measures to properly control infant mortality. We consider important to determine the reasons that are producing early death in this specific type of population, since they are the most vulnerable to high risk environmental conditions. In this way, the government, through competent authorities, may develop prevention policies and the right measures to avoid an increase of this tragic fact. The methodology used to develop this investigation is data mining, which consists in gaining and examining large amounts of data to produce new and valuable information. Through this technique it has been possible to determine that the child population is dying mostly from malnutrition. In short, this technique has been very useful to develop this study; it has allowed us to transform large amounts of information into a conclusive and important statement, which has made it easier to take appropriate steps to resolve a particular situation.

Keywords: Malnutrition, datamining, analytical, descriptive, population, wayuu, indigenous.

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Authors: Devasis Ghosh

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The main hindrance to total cure of cancer is a) the failure to control continued production of cancer cells, b) its sustenance and c) its metastasis. This review study has tried to address this issue of total cancer cure in a more innovative way. A 10-pronged “CRAB METHOD”, a novel holistic scientific approach of Cancer treatment has been hypothesized in this paper. Apart from available Chemotherapy, Radiotherapy and Oncosurgery, (which shall not be discussed here), seven other points of interference and treatment has been suggested, i.e. 1. Efficient stress management. 2. Dampening of ATF3 expression. 3. Selective inhibition of Platelet Activity. 4. Modulation of serotonin production, metabolism and 5HT receptor antagonism. 5. Auxin, its anti-proliferative potential and its modulation. 6. Melatonin supplementation because of its oncostatic properties. 7. HDAC Inhibitors especially valproic acid use due to its apoptotic role in many cancers. If all the above stated seven steps are thoroughly taken care of at the time of initial diagnosis of cancer along with the available treatment modalities of Chemotherapy, Radiotherapy and Oncosurgery, then perhaps, the morbidity and mortality rate of cancer may be greatly reduced.

Keywords: ATF3 dampening, auxin modulation, cancer, platelet activation, serotonin, stress, valproic acid.

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Authors: Saowaluk Sikiwat, Mayuree Pumipaiboon, Sutee Kaewsangiem, Mayuva Areekijseree

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The problem on the conservation programme of the Royal Thai Navy Sea Turtle Nursery, Phang-nga Province, Thailand is high mortality rate of juvenile green sea turtle (Cheloniamydas) on nursing period. So, during May to October 2012, postmortem examinations of juvenile green sea turtle were performed to determine the causes of dead. Fresh tissues of postmortem of 15 juvenile green sea turtles (1-3 months old) were investigated using paraffin section technique. The results showed normal ultrastructure of all tissue organs. These instances reviewed the health and stability of the environments in which juvenile green sea turtles live and concern for the survival rate. The present article also provides guidance for a review of the biology, guidelines for appropriate postmortem tissue, normal histology and sampling collection and procedures. The data also provides information for conservation of this endangered species in term of acknowledging and encouraging people to protect the animals and their habitats in nature.

Keywords: Green sea turtles (Cheloniamydas), histology, juvenile sea turtles

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Authors: Azmi Ibrahim, Nor Azan Mat Zin, Noraidah Sahari Ashaari

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Dengue fever is prevalent in Malaysia with numerous cases including mortality recorded over the years. Public education on the prevention of the desease through various means has been carried out besides the enforcement of legal means to eradicate Aedes mosquitoes, the dengue vector breeding ground. Hence, other means need to be explored, such as predicting the seasonal peak period of the dengue outbreak and identifying related climate factors contributing to the increase in the number of mosquitoes. Simulation model can be employed for this purpose. In this study, we created a simulation of system dynamic to predict the spread of dengue outbreak in Hulu Langat, Selangor Malaysia. The prototype was developed using STELLA 9.1.2 software. The main data input are rainfall, temperature and denggue cases. Data analysis from the graph showed that denggue cases can be predicted accurately using these two main variables- rainfall and temperature. However, the model will be further tested over a longer time period to ensure its accuracy, reliability and efficiency as a prediction tool for dengue outbreak.

Keywords: dengue fever, prediction, system dynamic, simulation

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Authors: Ali Rafiee, Ahad Salimi, Ali Reza Roosta

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Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound (TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a novel method for automatic prostate segmentation in TRUS images is presented. This method involves preprocessing (edge preserving noise reduction and smoothing) and prostate segmentation. The speckle reduction has been achieved by using stick filter and top-hat transform has been implemented for smoothing. A feed forward neural network and local binary pattern together have been use to find a point inside prostate object. Finally the boundary of prostate is extracted by the inside point and an active contour algorithm. A numbers of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with MSE less than 4.6% relative to boundary provided manually by physicians.

Keywords: Prostate segmentation, stick filter, neural network, active contour.

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Authors: Maher J. Tadros, Naji K. Al-Mefleh

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The evaluation of non-conventional water resources on seed germination and seedling growth performance at early growth stages is still in progress especially in forage crops. This study was designed to test the effect of four types of water qualities (treated wastewater (TWW), industrial water (IW), grey water (GW), and Distilled water (DW)) on germination and early seedling vigor of Leucaena leucocephala. The results showed that the germination was not significantly affected by the different water qualities. Seed germination reached maximum after 17, 14, 14, and 21 days under GW, IW, TWW, and DW treatments, respectively. The highest mean of shoot length was scored under the GW treatment. And, the highest mean of root length was scored under DW which was not significant from GW treatment. The means of shoot fresh was the highest under the TWW. The means of root fresh weight was not significantly different from each other's under different treatments. The growth performance was in progress with no mortality during 21 days of growth. Thus, the best non-conventional water qualities alternatives based on the cleanness, nutrients, and toxicity are the GW, TWW and IW, respectively.

Keywords: Seed germination, Growth performance, Leucaena, Multipurpose forest trees, Waste water, Grey water

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Authors: N. David, H. O. Gao

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Air pollution is a serious environmental threat on a global scale and can cause harm to human health, morbidity and premature mortality. Reliable monitoring and control systems are therefore necessary to develop coping skills against the hazards associated with this phenomenon. However, existing environmental monitoring means often do not provide a sufficient response due to practical and technical limitations. Commercial microwave links that form the infrastructure for transmitting data between cell phone towers can be harnessed to map rain at high tempo-spatial resolution. Rainfall causes a decrease in the signal strength received by these wireless communication links allowing it to be used as a built-in sensor network to map the phenomenon. In this study, we point to the potential that lies in this system to indirectly monitor areas where air pollution is reduced. The relationship between pollutant wash-off and rainfall provides an opportunity to acquire important spatial information about air quality using existing cell-phone tower signals. Since the density of microwave communication networks is high relative to any dedicated sensor arrays, it could be possible to rely on this available observation tool for studying precipitation scavenging on air pollutants, for model needs and more.

Keywords: Air pollution, commercial microwave links, rainfall.

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Authors: Yung-Ting Chang, Chung-You Tsai, Yu-Chuan Li

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Nosocomial (i.e., hospital-acquired) infections (NI) is a major cause of morbidity and mortality in hospitals. NI rate is higher in intensive care units (ICU) than in the general ward due to patients with severe symptoms, poor immunity, and accepted many invasive therapies. Contact behaviors between health caregivers and patients is one of the infect factors. It is difficult to obtain complete contact records by traditional method of retrospective analysis of medical records. This paper establishes a contact history inferential model (CHIM) intended to extend the use of Proximity Sensing of rapid frequency identification (RFID) technology to transferring all proximity events between health caregivers and patients into clinical events (close-in events, contact events and invasive events).The results of the study indicated that the CHIM can infer proximity care activities into close-in events and contact events. The infection control team could redesign and build optimal workflow in the ICU according to the patient-specific contact history which provided by our automatic tracing system.

Keywords: Active Radio Frequency Identification, Intensive Care Unit, Nosocomial Infections

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Authors: I. McCulloh, A. Placona, D. Stewart, D. Gause, K. Kiernan, M. Stuart, C. Zinner, L. Cartwright

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We find in our data that an alarming number of viable deceased donor kidneys are discarded every year in the US, while waitlisted candidates are dying every day. We observe as many as 85% of transplanted organs are refused at least once for a patient that scored higher on the match list. There are hundreds of clinical variables involved in making a clinical transplant decision and there is rarely an ideal match. Decision makers exhibit an optimism bias where they may refuse an organ offer assuming a better match is imminent. We propose a semi-parametric Cox proportional hazard model, augmented by an accelerated failure time model based on patient-specific suitable organ supply and demand to estimate a time-to-next-offer. Performance is assessed with Cox-Snell residuals and decision curve analysis, demonstrating improved decision support for up to a 5-year outlook. Providing clinical decision-makers with quantitative evidence of likely patient outcomes (e.g., time to next offer and the mortality associated with waiting) may improve decisions and reduce optimism bias, thus reducing discarded organs and matching more patients on the waitlist.

Keywords: Decision science, KDPI, optimism bias, organ transplant.

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Authors: A. Khalique, T. Naseem, N. Haque, Z. Rasool

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The objective of current study was to investigate the effect of Bacillus subtilis PB6 (CloSTAT) as a probiotic in broilers. The corn-soybean based diet was divided into four treatment groups; T1 (basal diet with no probiotic and no Clostridium perfringens); T2 (basal diet challenged with C. perfringens without probiotic); T3 (basal diet challenged with C. perfringens having 0.05% probiotic); T4 (basal diet challenged with C. perfringens having 0.1% probiotic). Every treatment group had four replicates with 24 birds each. Body weight and feed intake were measured on weekly basis, while ileal bacterial count was recorded on day-28 following Clostridium perfringens challenge. The 0.1% probiotic treatment showed 7.2% increase in average feed intake (P=0.05) and 8% increase in body weight compared to T2. In 0.1% treatment body weight was 5% higher than T3 (P=0.02). It was also observed that 0.1% treatment had improved feed conversion ratio (1.77) on 6^th week. No effect of treatment was observed on mortality and ileal bacterial count. The current study indicated that 0.1% use of probiotic had positive response in C. perfringens challenged broilers.

Keywords: Bacillus subtilis PB6, antibiotic growth promoters, Clostridium perfringens, CloSTAT, broilers.

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Authors: Abdulwahab Kammon, Tan Sheau Wei, Abdul Rahman Omar, Abdunaser Dayhum, Ibrahim Eldghayes, Monier Sharif

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Infectious bronchitis virus (IBV) is a very dynamic and evolving virus, causing major economic losses to the global poultry industry. Recently, the Libyan poultry industry faced severe outbreak of respiratory distress associated with high mortality and dramatic drop in egg production. Tracheal and cloacal swabs were analyzed for several poultry viruses. IBV was detected using SYBR Green I real-time PCR detection based on the nucleocapsid (N) gene. Sequence analysis of the partial N gene indicated high similarity (~ 94%) to IBV strain 3382/06 that was isolated from Taiwan. Even though the IBV strain 3382/06 is more similar to that of the Mass type H120, the isolate has been implicated associated with intertypic recombinant of 3 putative parental IBV strains namely H120, Taiwan strain 1171/92 and China strain CK/CH/LDL/97I. Complete sequencing and antigenicity studies of the Libya IBV strains are currently underway to determine the evolution of the virus and its importance in vaccine induced immunity. In this paper we documented for the first time the presence of possibly variant IBV strain from Libya which required dramatic change in vaccination program.

Keywords: Libya, Infectious bronchitis, Molecular characterization.

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: Cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma.

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Authors: D. M. Onyango, P. O. Angienda

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The purpose of the present study was to evaluate the epidemiology of waterborne diarrhoeal among children aged 6-36 months old in Busia town, western Kenya. The study was carried out between Feb. 2008 and Feb. 2010. Cases of diarrhoea reported in 385 households were linked to household water handling practices. A mother with a child of 6-36 months old was also included in the study. Diarrhoea prevalence among children 6-36 months was 16.7% in Busia town, Bwamani (19.6%) and Mayenje (10.6%) clustered in Mayenje sub-location reported the highest and the lowest prevalence of diarrhoea. There was a positive correlation between the prevalence of diarrhoea in children and the level of the mother-s education, 29.9% (n= 100). Diarrhoea cases decreased in range from 35.5% (n =102) to 4.8% (n= 16), corresponding to increase in age from 6-35 months on average. In conclusion, prevalence of diarrhoea in children of 6-36 months old was 16.7% in Busia town. This was higher in children whose mother-s age was below 18 years and with low level of education, the rate decreased with increase in age of children. Prevalence of diarrhoea in children aged 6-36months in households was higher in children aged 6-17 and 36 months and whose mothers were less educated and fell between the ages of 18-24 years. The Influence of human activities at the main source of drinking water on the prevalence of diarrhoea in these children was insignificant.

Keywords: Diarrhoea, Children, Mortality, Waterborne disease,

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Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

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During the past decade, pond aeration systems have been developed which will sustain large quantities of fish and invertebrate biomass. Dissolved Oxygen (DO) is considered to be among the most important water quality parameters in fish culture. Fishponds in aquaculture farms are usually located in remote areas where grid lines are at far distance. Aeration of ponds is required to prevent mortality and to intensify production, especially when feeding is practical, and in warm regions. To increase pond production it is necessary to control dissolved oxygen. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. This paper presents a new design of diffused aeration system using fuel cell as a power source. Also fuzzy logic control Technique (FLC) is used for controlling the speed of air flow rate from the blower to air piping connected to the pond by adjusting blower speed. MATLAB SIMULINK results show high performance of fuzzy logic control (FLC).

Keywords: aeration system, Fuel cell, Artificial intelligence (AI) techniques, fuzzy logic control

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Authors: Funmilola O. Omoya

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Muscid flies are known to be vectors of disease agents and species that annoy humans and domesticated animals. An example of these flies is Musca domestica (house fly) whose adult and immature stages occur in a variety of filthy organic substances including household garbage and animal manures. They contribute to microbial contamination of foods. It is therefore imperative to control these flies as a result of their role in Public health. The second and third instars of Musca domestica (Linn) were infected with varying cell loads of Bacillus subtilis in vitro for a period of 48 hours to evaluate its larvicidal activities. Mortality of the larvae increased with incubation period after treatment with the varying cell loads. Investigation revealed that the second instars larvae were more susceptible to treatment than the third instars treatments. Values obtained from the third instar group were significantly different (P<0.05) from those obtained from the second instars group in all the treatments. Lethal concentration (LC₅₀) at 24 hours for 2^nd instars was 2.35 while LC₅₀ at 48 hours was 4.31.This study revealed that Bacillus subtilis possess good larvicidal potential for use in the control of Musca domestica in poultry farms.

Keywords: Bacillus subtilis, larvicidal activities, Musca domestica, poultry farms.

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Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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Authors: Muhammad Shahid, Nasir-uddin Khan, Mushtaq Hussain, Muhammad Liaquat Ali, Asif Mansoor

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Tuberculosis (TB) remains a leading cause of infectious mortality. It is primarily transmitted by the respiratory route, individuals with active disease may infect others through airborne particles which releases when they cough, talk, or sing and subsequently inhale by others. In order to study the effect of the Bacilli Calmette-Guerin (BCG) vaccine after vaccination of TB patient, a Vaccinated Susceptible Infected and Recovered (VSIR) mathematical model is being developed to achieve the desired objectives. The mathematical model, so developed, shall be used to quantify the effect of BCG Vaccine to protect the immigrant young adult person. Moreover, equations are to be established for the disease endemic and free equilibrium states and subsequently utilized in disease stability analysis. The stability analysis will give a complete picture of disease annihilation from the total population if the total removal rate from the infectious group should be greater than total number of dormant infections produced throughout infectious period.

Keywords: Bacillus Calmette-Guerin vaccine, disease-free equilibrium state, VSIR Quantification, disease stability analysis.

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: Medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis.

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Authors: Ling-Lih Shen, Feng-Chuan Pan

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Chronic diseases prevailed along with economic growth as well as life style changed in recent years in Taiwan. According to the governmental statistics, hypertension related disease is the tenth of death causes with 1,816 died directly from hypertension in 2010. There were more death causes amongst the top ten had been proofed that having strong association with the hypertension, such as heart diseases, cardiovascular diseases, and diabetes. Hypertension or High blood pressure is one of the major indicators for chronic diseases, and was generally perceived as the major causes of mortality. The literature generally suggested that regular physical exercise was helpful to prevent the occurrence or to ease the progress of a hypertension. This paper reported the process and outcomes in detailed of an improvement project of physical exercise intervention specific for hypertension patients. Physical information were measured before and after the project to obtain information such as weight, waistline, cholesterol (HD & LD), blood examination, as well as self-perceived health status. The intervention project involved a six-week exercise program, of which contained three times a week, 30 minutes of tutored physical exercise intervention. The project had achieved several gains in changing the subjects- behavior in terms of many important biophysical indexes. Around 20% of the participants had significantly improved their cholesterols, BMI, and changed unhealthy behaviors. Results from the project were encouraging, and would be good reference for other samples.

Keywords: Intervention, biological information, hypertension patients, behavioral changes, chronic disease

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Authors: Amina M. Bishr, Ahmed B. Magdub, Abdul-Baset R. Abuzweda

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A major challenge in camel productivity is the high mortality rate of camel calves in the early stage due to the lack of colostrums. This study investigates the time required for the calves to obtain the optimum amount of the immunoglobulin (IgG). Eleven pregnant female camels (Camelus Dromedarus) were selected randomly and variant in age and gestation. After delivery, 7 calves were obtained and used for this investigation. Colostrum samples were collected from mothers immediately after parturition. Blood samples were obtained from the calves as follow: 0 day (before suckling), 24, 48, 72, 96, 120 and 144 hours, 2nd, 3rd, and 4th weeks post suckling. Blood serum and colostrums whey were separated and used to determine IgG concentration, total protein and concentration of Cortisol and Thyroxin. The results showed high levels of IgG in camel colostrums (328.8 ± 4.5 mg / ml). The IgG concentration in serum of calves was the highest within 1st 24 h after suckling (140.75 mg /ml), and then declined gradually reached lower level at 144 h (41.97 mg / ml). The average turnover rate (t 1/2) of serum IgG in the all cases was 3.22 days. The turnover of ranged from 2.56 days for calves have values of IgG more than average and 7.7 days for those with values below average. In spite of very high levels of thyroxin in sera of new born the results showed no correlation between cortisol and thyroxin with IgG levels.

Keywords: Camel, cortisol, IgG, thyroxin, turn-over rate.

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Authors: Zuhrotun Ulya, Muchammad Syamsulhadi, Debree Septiawan

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Patients with bipolar disorder are three times more likely to suffer cardiovascular disorders than the general population, which will influence their level of morbidity and rate of mortality. Bipolar disorder also affects the pulmonary system. The choice of long term-monotherapy and other combinative therapies have clinical impacts on patients. This study investigates the case of a woman who has been suffering from bipolar disorder for 16 years, and who has a history of Steven Johnson Syndrome. At present she is suffering also from cardiovascular and pulmonary disorder. An analysis of the results of this study suggests that there is a relationship between cardiovascular disorder, drug therapies, Steven Johnson Syndrome and mood stabilizer obtained from the PubMed, Cochrane, Medline, and ProQuest (publications between 2005 and 2015). Combination therapy with mood stabilizer is recommended for patients who do not have side effect histories from these drugs. The replacement drugs and combinations may be applied, especially for those with bipolar disorders, and the combination between atypical antipsychotic groups and mood stabilizers is often made. Clinicians, however, should be careful with the patients’ physical and metabolic changes, especially those who have experienced long-term therapy and who showed a history of Steven Johnson Syndrome (for which clinicians probably prescribed one type of medicine).

Keywords: Cardio-pulmonary disease, bipolar disorder, Steven Johnson Syndrome, therapy.

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Authors: A. Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

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Temperature is one of the most principle factors affects aquaculture system. It can cause stress and mortality or superior environment for growth and reproduction. This paper presents the control of pond water temperature using artificial intelligence technique. The water temperature is very important parameter for shrimp growth. The required temperature for optimal growth is 34oC, if temperature increase up to 38oC it cause death of the shrimp, so it is important to control water temperature. Solar thermal water heating system is designed to supply an aquaculture pond with the required hot water in Mersa Matruh in Egypt. Neural networks are massively parallel processors that have the ability to learn patterns through a training experience. Because of this feature, they are often well suited for modeling complex and non-linear processes such as those commonly found in the heating system. Artificial neural network is proposed to control water temperature due to Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques. They have been used to solve complicated practical problems. Moreover this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the aquaculture system. The simulation results indicate that, the control unit success in keeping water temperature constant at the desired temperature by controlling the hot water flow rate.

Keywords: artificial neural networks, aquaculture, forced circulation hot water system,

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Authors: Rassoli Aisa, Abrishami Movahhed Arezu, Faturaee Nasser, Seddighi Amir Saeed, Shafigh Mohammad

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Cardiovascular diseases are one of the most common causes of mortality in developed countries. Coronary artery abnormalities and carotid artery stenosis, also known as silent death, are among these diseases. One of the treatment methods for these diseases is to create a deviatory pathway to conduct blood into the heart through a bypass surgery. The saphenous vein is usually used in this surgery to create the deviatory pathway. Unfortunately, a re-surgery will be necessary after some years due to ignoring the disagreement of mechanical properties of graft tissue and/or applied prostheses with those of host tissue. The objective of the present study is to clarify the viscoelastic behavior of human saphenous tissue. The stress relaxation tests in circumferential and longitudinal direction were done in this vein by exerting 20% and 50% strains. Considering the stress relaxation curves obtained from stress relaxation tests and the coefficients of the standard solid model, it was demonstrated that the saphenous vein has a non-linear viscoelastic behavior. Thereafter, the fitting with Fung’s quasilinear viscoelastic (QLV) model was performed based on stress relaxation time curves. Finally, the coefficients of Fung’s QLV model, which models the behavior of saphenous tissue very well, were presented.

Keywords: Fung’s quasilinear viscoelastic (QLV) model, strain rate, stress relaxation test, uniaxial tensile test, viscoelastic behavior.

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Authors: Walid MOUDANI

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts- knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: Multi-Classifier Decisions Tree, Features Reduction, Dynamic Programming, Rough Sets.

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Authors: Anders S. Kristensen, Dewan Ahsan, Saqib Mehmood, Shakeel Ahmed

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Traffic accidents are a result of the convergence of hazards, malfunctioning of vehicles and human negligence that have adverse economic and health impacts and effects. Unfortunately, avoiding them completely is very difficult, but with quick response to rescue and first aid, the mortality rate of inflicted persons can be reduced significantly. Smart and innovative technologies can play a pivotal role to respond faster to traffic crash emergencies comparing conventional means of transportation. For instance, Rescue Emergency Drone (RED) can provide faster and real-time crash site risk assessment to emergency medical services, thereby helping them to quickly and accurately assess a situation, dispatch the right equipment and assist bystanders to treat inflicted person properly. To conduct a research in this regard, the case of a traffic roundabout that is prone to frequent traffic accidents on the outskirts of Esbjerg, a town located on western coast of Denmark is hypothetically considered. Along with manual calculations, Emergency Disaster Management Simulation (EDMSIM) has been used to verify the response time of RED from a fire station of the town to the presumed crash site. The results of the study demonstrate the robustness of RED into emergency services to help save lives. 

Keywords: Automated external defibrillator, medical emergency, fire and rescue services, response time, unmanned aerial system.

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Authors: Funmilola O. Omoya, Abdul O. Momoh

Abstract:

Malaria constitutes one of the major health problems in Nigeria. One of the reasons attributed for the upsurge was the development of resistance of Plasmodium falciparum and the emergence of multi-resistant strains of the parasite to anti-malaria drugs. A continued search for other effective, safe and cheap plantbased anti-malaria agents thus becomes imperative in the face of these difficulties. The objective of this study is therefore to evaluate the in vivo anti-malarial efficacy of ethanolic extracts of Chromolaena odorata and Androgaphis paniculata leaves. The two plants were evaluated for their anti-malaria efficacy in vivo in a 4-day curative test assay against Plasmodium berghei strain in mice. The group treated with 500mg/ml dose of ethanolic extract of A. paniculata plant showed parasite suppression with increase in Packed Cell Volume (PCV) value except day 3 which showed a slight decrease in PCV value. During the 4-day curative test, an increase in the PCV values, weight measurement and zero count of Plasmodium berghei parasite values was recorded after day 3 of drug administration. These results obtained in group treated with A. paniculata extract showed anti-malarial efficacy with higher mortality rate in parasitaemia count when compared with Chromolaena odorata group. These results justify the use of ethanolic extracts of A. paniculata plant as medicinal herb used in folklore medicine in the treatment of malaria.

Keywords: Anti-malaria, Curative, Plant-based anti-malaria agents.

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Authors: H. M. Abdelmoneim, N. A. Babtain, A. S. Barhamain, A. Z. Kufiah, A. S. Malibari, S. F. Munassar, R. S. Rawa

Abstract:

Introduction: Prostate cancer is one of the most common causes of morbidity and mortality in men in developed countries. Cancer Stem Cells (CSCs) could be responsible for the progression and relapse of cancer. Therefore, CSCs markers could provide a prognostic strategy for human malignancies. Aldehyde dehydrogenase 1A1 (ALDH1A1) activity has been shown to be associated with tumorigenesis and proposed to represent a functional marker for tumor initiating cells in various tumor types including prostate cancer. Material & Methods: We analyzed the immunohistochemical expression of ALDH1A1 in benign prostatic hyperplasia (BPH), prostatic intraepithelial neoplasia (PIN) and prostatic adenocarcinoma and assessed their significant correlations in 50 TURP sections. They were microscopically interpreted and the results were correlated with histopathological types and tumor grade. Results: In different prostatic histopathological lesions we found that ALDH1A1 expression was low in BPH (13.3%) and PIN (6.7%) and then its expression increased with prostatic adenocarcinoma (40%), and this was statistically highly significant (P value = 0.02). However, in different grades of prostatic adenocarcinoma we found that the higher the Gleason grade the higher the expression for ALDH1A1 and this was statistically significant (P value = 0.02). We compared the expression of ALDH1A1 in PIN and prostatic adenocarcinoma. ALDH1A1 expression was decreased in PIN and highly expressed in prostatic adenocarcinoma and this was statistically significant (P value = 0.04). Conclusion: Increasing ALDH1A1 expression is correlated with aggressive behavior of the tumor. Immunohistochemical expression of ALDH1A1 might provide a potential approach to study tumorigenesis and progression of primary prostate carcinoma.

Keywords: ALDH1A1, BPH, PIN, prostatic adenocarcinoma.

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

Abstract:

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

Abstract:

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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