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Articles 1 - 30 of 46
Full-Text Articles in Medicine and Health Sciences
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
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Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …
Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa
Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa
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BACKGROUND: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to …
Empowering Patient Similarity Networks Through Innovative Data-Quality-Aware Federated Profiling, Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb
Empowering Patient Similarity Networks Through Innovative Data-Quality-Aware Federated Profiling, Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb
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Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the …
Personalized Health Care In A Data-Driven Era: A Post–Covid-19 Retrospective, Arnob Zahid, Ravishankar Sharma
Personalized Health Care In A Data-Driven Era: A Post–Covid-19 Retrospective, Arnob Zahid, Ravishankar Sharma
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No abstract provided.
Cardiac Arrhythmia Disease Classifier Model Based On A Fuzzy Fusion Approach, Fatma Taher, Hamoud Alshammari, Lobna Osman, Mohamed Elhoseny, Abdulaziz Shehab, Eman Elayat
Cardiac Arrhythmia Disease Classifier Model Based On A Fuzzy Fusion Approach, Fatma Taher, Hamoud Alshammari, Lobna Osman, Mohamed Elhoseny, Abdulaziz Shehab, Eman Elayat
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Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, …
Intelligent Health Care And Diseases Management System: Multi-Day-Ahead Predictions Of Covid-19, Ahed Abugabah, Farah Shahid
Intelligent Health Care And Diseases Management System: Multi-Day-Ahead Predictions Of Covid-19, Ahed Abugabah, Farah Shahid
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The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As a case study, the total number of death cases in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning and control. In this paper, the proposed model for making a time-series forecast for daily and total infected cases, death cases, and recovered cases for the countrywide Algeria COVID-19 dataset is a two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used to assess the model’s performance: mean absolute error (MAE), root …
A Deep Learning Based Dual Encoder–Decoder Framework For Anatomical Structure Segmentation In Chest X-Ray Images, Ihsan Ullah, Farman Ali, Babar Shah, Shaker El-Sappagh, Tamer Abuhmed, Sang Hyun Park
A Deep Learning Based Dual Encoder–Decoder Framework For Anatomical Structure Segmentation In Chest X-Ray Images, Ihsan Ullah, Farman Ali, Babar Shah, Shaker El-Sappagh, Tamer Abuhmed, Sang Hyun Park
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Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 …
Fast Covid-19 Detection From Chest X-Ray Images Using Dct Compression, Fatma Taher, Reem T. Haweel, Usama M. H. Al Bastaki, Eman Abdelwahed, Tariq Rehman, Tarek I. Haweel
Fast Covid-19 Detection From Chest X-Ray Images Using Dct Compression, Fatma Taher, Reem T. Haweel, Usama M. H. Al Bastaki, Eman Abdelwahed, Tariq Rehman, Tarek I. Haweel
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Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and …
Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
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In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the …
Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz
Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz
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Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on …
Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah
Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah
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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can …
Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi
Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi
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COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …
Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
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This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. …
Segmentation Of Infant Brain Using Nonnegative Matrix Factorization, Norah Saleh Alghamdi, Fatma Taher, Heba Kandil, Ahmed Sharafeldeen, Ahmed Elnakib, Ahmed Soliman, Yaser Elnakieb, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz
Segmentation Of Infant Brain Using Nonnegative Matrix Factorization, Norah Saleh Alghamdi, Fatma Taher, Heba Kandil, Ahmed Sharafeldeen, Ahmed Elnakib, Ahmed Soliman, Yaser Elnakieb, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz
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This study develops an atlas-based automated framework for segmenting infants' brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant's brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix …
A Non-Invasive Interpretable Diagnosis Of Melanoma Skin Cancer Using Deep Learning And Ensemble Stacking Of Machine Learning Models, Iftiaz A. Alfi, Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazir
A Non-Invasive Interpretable Diagnosis Of Melanoma Skin Cancer Using Deep Learning And Ensemble Stacking Of Machine Learning Models, Iftiaz A. Alfi, Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazir
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A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in …
A Novel Text Mining Approach For Mental Health Prediction Using Bi-Lstm And Bert Model, Kamil Zeberga, Muhammad Attique, Babar Shah, Farman Ali, Yalew Zelalem Jembre, Tae-Sun Chung
A Novel Text Mining Approach For Mental Health Prediction Using Bi-Lstm And Bert Model, Kamil Zeberga, Muhammad Attique, Babar Shah, Farman Ali, Yalew Zelalem Jembre, Tae-Sun Chung
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With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify …
Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura
Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura
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Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In …
Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni
Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni
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The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …
Automatic Cerebrovascular Segmentation Methods - A Review, Fatma Taher, Neema Prakash
Automatic Cerebrovascular Segmentation Methods - A Review, Fatma Taher, Neema Prakash
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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography …
Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz
Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz
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The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or …
Gaming Disorder And Well-Being Among Emirati College Women, Marina Verlinden, Justin Thomas, Mahra Hasan Abdulla Ahamed Almansoori, Shamil Wanigaratne
Gaming Disorder And Well-Being Among Emirati College Women, Marina Verlinden, Justin Thomas, Mahra Hasan Abdulla Ahamed Almansoori, Shamil Wanigaratne
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Background: The present study examined Internet Gaming Disorder (IGD) and depressive symptom levels among a predominantly female sample of college students from the United Arab Emirates (UAE). Methods: IGD was assessed among two successive cohorts of students at the beginning of the academic year in 2016 and 2019, respectively. All participants (n = 412) completed the Internet Gaming Disorder Scale – Short-Form (IGDS9-SF) and the WHO-5 Well-being Index (WHO-5), a tool widely used for the screening and assessment of depressive symptomatology. Results: Mean IGDS9-SF scores (15.85, SD = 6.40) were fairly similar to those observed in other nations. The prevalence …
A Novel Mra-Based Framework For Segmenting The Cerebrovascular System And Correlating Cerebral Vascular Changes To Mean Arterial Pressure, Fatma Taher, Heba Kandil, Yitzhak Gebru, Ali Mahmoud, Ahmed Shalaby, Shady El‐Mashad, Ayman El‐Baz
A Novel Mra-Based Framework For Segmenting The Cerebrovascular System And Correlating Cerebral Vascular Changes To Mean Arterial Pressure, Fatma Taher, Heba Kandil, Yitzhak Gebru, Ali Mahmoud, Ahmed Shalaby, Shady El‐Mashad, Ayman El‐Baz
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Blood pressure (BP) changes with age are widespread, and systemic high blood pressure (HBP) is a serious factor in developing strokes and cognitive impairment. A non‐invasive methodology to detect changes in human brain’s vasculature using Magnetic Resonance Angiography (MRA) data and correlation of cerebrovascular changes to mean arterial pressure (MAP) is pre-sented. MRA data and systemic blood pressure measurements were gathered from patients (n = 15, M = 8, F = 7, Age = 49.2 ± 7.3 years) over 700 days (an initial visit and then a follow‐up period of 2 years with a final visit.). A novel segmentation algorithm …
A Comprehensive Review Of Retinal Vascular And Optical Nerve Diseases Based On Optical Coherence Tomography Angiography, Fatma Taher, Heba Kandil, Hatem Mahmoud, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Marah Talal Alhalabi, Harpal Singh Sandhu, Ayman El-Baz
A Comprehensive Review Of Retinal Vascular And Optical Nerve Diseases Based On Optical Coherence Tomography Angiography, Fatma Taher, Heba Kandil, Hatem Mahmoud, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Marah Talal Alhalabi, Harpal Singh Sandhu, Ayman El-Baz
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The optical coherence tomography angiography (OCTA) is a noninvasive imaging technology which aims at imaging blood vessels in retina by studying decorrelation signals between multiple sequential OCT B-scans captured in the same cross section. Obtaining various vascular plexuses including deep and superficial choriocapillaris, is possible, which helps in understanding the ischemic processes that affect different retina layers. OCTA is a safe imaging modality that does not use dye. OCTA is also fast as it can capture high-resolution images in just seconds. Additionally, it is used in the assessment of structure and blood flow. OCTA provides anatomic details in addition to …
Modulatory And Toxicological Perspectives On The Effects Of The Small Molecule Kinetin, Eman M. Othman, Moustafa Fathy, Amany Abdlrehim Bekhit, Abdel-Razik H. Abdel-Razik, Arshad Jamal, Yousef Nazzal, Shabana Shams, Thomas Dandekar, Muhammad Naseem
Modulatory And Toxicological Perspectives On The Effects Of The Small Molecule Kinetin, Eman M. Othman, Moustafa Fathy, Amany Abdlrehim Bekhit, Abdel-Razik H. Abdel-Razik, Arshad Jamal, Yousef Nazzal, Shabana Shams, Thomas Dandekar, Muhammad Naseem
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Plant hormones are small regulatory molecules that exert pharmacological actions in mammalian cells such as anti-oxidative and pro-metabolic effects. Kinetin belongs to the group of plant hormones cytokinin and has been associated with modulatory functions in mammalian cells. The mammalian adenosine receptor (A2a-R) is known to modulate multiple physiological responses in animal cells. Here, we describe that kinetin binds to the adenosine receptor (A2a-R) through the Asn253 residue in an adenosine dependent manner. To harness the beneficial effects of kinetin for future human use, we assess its acute toxicity by analyzing different biochemical and histological markers in rats. Kinetin at …
Cardiac Inflammation, Oxidative Stress, Nrf2 Expression, And Coagulation Events In Mice With Experimental Chronic Kidney Disease, Abderrahim Nemmar, Suhail Al-Salam, Sumaya Beegam, Nur Elena Zaaba, Javed Yasin, Naserddine Hamadi, Badreldin H. Ali
Cardiac Inflammation, Oxidative Stress, Nrf2 Expression, And Coagulation Events In Mice With Experimental Chronic Kidney Disease, Abderrahim Nemmar, Suhail Al-Salam, Sumaya Beegam, Nur Elena Zaaba, Javed Yasin, Naserddine Hamadi, Badreldin H. Ali
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Chronic kidney disease (CKD) is known to be associated with cardiovascular dysfunction. Dietary adenine intake in mice is also known to induce CKD. However, in this experimental model, the mechanisms underlying the cardiotoxicity and coagulation disturbances are not fully understood. Here, we evaluated cardiac inflammation, oxidative stress, DNA damage, and coagulation events in mice with adenine (0.2% w/w in feed for 4 weeks)-induced CKD. Control mice were fed with normal chow for the same duration. Adenine increased water intake, urine output, relative kidney weight, the plasma concentrations of urea and creatinine, and the urinary concentrations of kidney injury molecule-1 and …
A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita
A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita
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The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine …
Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri
Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri
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The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit …
Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet
Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet
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Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain …
Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi
Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi
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Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance …
The Impact Of Herbal Infusion Consumption On Oxidative Stress And Cancer: The Good, The Bad, The Misunderstood, Wamidh H. Talib, Israa A. Al-Ataby, Asma Ismail Mahmod, Sajidah Jawarneh, Lina T. Al Kury, Intisar Hadi Al-Yasari
The Impact Of Herbal Infusion Consumption On Oxidative Stress And Cancer: The Good, The Bad, The Misunderstood, Wamidh H. Talib, Israa A. Al-Ataby, Asma Ismail Mahmod, Sajidah Jawarneh, Lina T. Al Kury, Intisar Hadi Al-Yasari
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The release of reactive oxygen species (ROS) and oxidative stress is associated with the development of many ailments, including cardiovascular diseases, diabetes and cancer. The causal link between oxidative stress and cancer is well established and antioxidants are suggested as a protective mechanism against cancer development. Recently, an increase in the consumption of antioxidant supplements was observed globally. The main sources of these antioxidants include fruits, vegetables, and beverage. Herbal infusions are highly popular beverages consumed daily for different reasons. Studies showed the potent antioxidant effects of plants used in the …