Immunogenetics of Type 1 diabetes and Celiac disease

AbstractThe primary purpose of understanding disease etiology is to explain how a specific phenotype is determined by genotype. In pursue of this aim, exploring the diversity in DNA sequence variants that affect biomedical traits, especially those related to the onset and progression of genetically determined human disease. The human leukocyte antigens (HLA) are highly polymorphic cell surface proteins encoded in the major histocompatibility complex (MHC) region on chromosome 6. The HLA molecules are integral regulators for susceptibility to several autoimmune and inflammatory diseases, including type 1 diabetes (T1D) and celiac disease (CD), which share high-risk HLA haplotypes. Through next-generation sequencing (NGS), an integrated genotyping system of HLA loci was developed to genotype alleles of the MHC region. The full depth of allele association was used to target the novel mechanisms of HLA-associated risk alleles in T1D and CD. The research presented in this thesis aimed to use high-resolution genotyping with NGS of HLA loci and study extended associations in patients with T1D and CD as well as in a group of patients affected by both diseases (T1D w/CD). The main findings of importance were: -• HLA-DRB3, DRB4, and DRB5 affect the risk of islet autoimmunity and progression to the clinical onset of T1D and should be considered when examining the role of HLA-DR genetic risk. • Two distinct CD risk DR3-DQA1*05:01-DQB*02:01 haplotypes distinguished by either HLA-DRB3*01:01:02 and DRB3*02:02:01 alleles in the DRB3*01:01:02- DQA1*05:01-DQB1*02:01 extended haplotype distinguished the risk of CD, indicating that different DRB1*03:01-DQB1*02:01 haplotypes confer different risks for CD among patients of Scandinavian background. • HLA-DRB4*01:03:01, DRB3*01:01:02, and DRB3*02:02:01 are associated with T1D and CD of which DRB4*01:03:01 confers the strongest risk allele for T1D w/CD.• HLA-A*68:01:02 was identified as an additional allele positively associated between T1D w/CD and T1D. In conclusion, by utilizing high-resolution sequencing technologies for extended genotyping of HLA class I and II genetic determinants, the full spectrum of alleles and haplotypes variation associated with T1D and CD were explored. This basic knowledge should prove helpful contribution in building comprehensive inventories of genotype-phenotype relationships and resolving some of the HLA roles in the heritability risk for either T1D or CD, as well as in genetic models for the risk of developing both diseases

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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, for the miss-valued attributes, we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes. However, in order to ensure the model optimality, we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers. The preprocessing step led to 161 out of 279 attributes (features). Thereafter, a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms. In short, our study comprises three main blocks: (1) sensing data and preprocessing; (2) feature queuing, selection, and extraction; and (3) the predictive model. Our proposed method improves classification performance in terms of accuracy, F1 measure, recall, and precision when compared to state-of-the-art techniques. It achieves 98.5% accuracy for binary class mode and 98.9% accuracy for categorized class mode

Types of glaucoma in a university health centre in Al‑Ahsa, Saudi Arabia: a pilot study

Objective: The objective was to assess the profile of different types of glaucoma in a University Health Centre in Al‑Ahsa, Saudi Arabia.Materials and Methods: It is a retrospective study in which the files of the patients at King Faisal University Health Centre were reviewed. The data collected included: Age, sex, race, visual acuity, the slit lamp examination findings, the intraocular pressure (IOP) as the average of 3 readings, the cup‑to‑disc ratio (CDR), the visual field changes, and the details of treatment received.Results: Eighty glaucomatous eyes from 50 patients were included in the study. The mean age was 54.8 ± 12.7 years, and the mean IOP was 19 ± 3.9 mmHg that ranged from 11 to 28 mmHg. The mean CDR mean was 0.48 ± 0.16 that ranged between 0.3 and 0.9. Ninety‑one percent of the visual field defects were arcuate scotomata. Primary open‑angle glaucoma (POAG) (60%) was the most predominant type of glaucoma, followed by primary angle closure glaucoma (ACG) (21.3%), secondary OAG (7.5%), and secondary ACG (6.3%). As for the anti‑glaucoma medications, 88% of the studied patients were on more than one medicine.Conclusion: This pilot study has demonstrated that POAG may be the predominant type of glaucoma in Al‑Ahsa, Kingdom of Saudi Arabia (KSA). Apopulation‑based study with a larger sample size is warranted to confirm the outcome and to provide a baseline data on the prevalence of types of glaucoma in this region of KSA.Keywords: Glaucoma, glaucoma types, prevalence, Saudi Arabi

COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images

The COVID-19 pandemic has a significant negative effect on people’s health, as well as on the world’s economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers’ attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure

Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time

Judicial control over public administration in the United Arab Emirates

This study highlights public administration’s role and position in the United Arab Emirates. Drawing on the literature on judicial interventions in public administration and the freedom and rights of citizens in the United Arab Emirates, this study attempts to develop greater understanding regarding the balance between the two bodies. It analyzes articles, journals, and government reports to understand the traditional judicial relationship with public administration and the distribution of freedoms among public interests; it also attempts to detect any changes to this system in the context of the United Arab Emirates. The study discusses the application of law, the freedoms contained within public prosecution, examining how the government is renovating the best countries in terms of legal systems to make the United Arab Emirates one of the best countries in terms of legal systems

2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop

Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time