Targeted screening for primary immunodeficiency disorders in the neonatal period and early infancy

Background: Primary immunodeficiency diseases (PID) comprise a group of more than 300 diseases that affect development and /or function of the immune system.Objectives: The aim of this study was diagnosis of PID among a suspected group of neonates and infants within the first six months of life as well as identifying the warning signs of PID characteristic to this period.Method: Fifty neonates presenting with warning signs of PID were enrolled in the study.Results: The study revealed that twenty six patients (52%) were diagnosed with Primary Immunodeficiency, T cell/combined immunodeficiency were noted as the most common PID class (88.5%) with fourteen T-B-SCID patients (70%) and six T-B+ SCID patients (30%), phagocytic disorders were estimated to be 7.7% while 3.8% were unclassified immunodeficiency. The mean age of presentation for PID group was 1.42±1.38 months with a diagnostic lag of 3.08±1.78 months. Consanguinity was positive in 76.9% of the PID group. Lower respiratory tract infections ,persistent fungal infections and lymphopenia were the most significant warning signs for diagnosing PID with a p value of (0.01). Combined, lower respiratory tract infections, fungal infections and lymphopenia were 12.3 times more likely to be associated with PID.Conclusion: Focused screening in high risk neonates proved to be a valuable tool for diagnosis of PID disorders.Keywords: Primary immunodeficiency disorders, neonatal period, early infancy

Targeted screening for primary immunodeficiency disorders in the neonatal period and early infancy

Background: Primary immunodeficiency diseases (PID) comprise a group of more than 300 diseases that affect development and /or function of the immune system. Objectives: The aim of this study was diagnosis of PID among a suspected group of neonates and infants within the first six months of life as well as identifying the warning signs of PID characteristic to this period. Method: Fifty neonates presenting with warning signs of PID were enrolled in the study. Results: The study revealed that twenty six patients (52%) were diagnosed with Primary Immunodeficiency, T cell/combined immunodeficiency were noted as the most common PID class (88.5%) with fourteen T-B-SCID patients (70%) and six T-B+SCID patients (30%), phagocytic disorders were estimated to be 7.7% while 3.8% were unclassified immunodeficiency. The mean age of presentation for PID group was 1.42\ub11.38 months with a diagnostic lag of 3.08\ub11.78 months. Consanguinity was positive in 76.9% of the PID group. Lower respiratory tract infections ,persistent fungal infections and lymphopenia were the most significant warning signs for diagnosing PID with a p value of (0.01). Combined, lower respiratory tract infections, fungal infections and lymphopenia were 12.3 times more likely to be associated with PID. Conclusion: Focused screening in high risk neonates proved to be a valuable tool for diagnosis of PID disorders. DOI: https://dx.doi.org/10.4314/ahs.v19i1.18 Cite as: Galal N, Ohida M, Meshaal S, Abd Elaziz D, I E. Targeted screening for primary immunodeficiency disorders in the neonatal period and early infancy. Afri Health Sci. 2019;19(1). 1449-1459. https://dx.doi. org/10.4314/ahs.v19i1.1

Targeted screening for primary immunodeficiency disorders in the neonatal period and early infancy

Background: Primary immunodeficiency diseases (PID) comprise a group of more than 300 diseases that affect development and /or function of the immune system. Objectives: The aim of this study was diagnosis of PID among a suspected group of neonates and infants within the first six months of life as well as identifying the warning signs of PID characteristic to this period. Method: Fifty neonates presenting with warning signs of PID were enrolled in the study. Results: The study revealed that twenty six patients (52%) were diagnosed with Primary Immunodeficiency, T cell/combined immunodeficiency were noted as the most common PID class (88.5%) with fourteen T-B-SCID patients (70%) and six T-B+ SCID patients (30%), phagocytic disorders were estimated to be 7.7% while 3.8% were unclassified immunodeficiency. The mean age of presentation for PID group was 1.42\ub11.38 months with a diagnostic lag of 3.08\ub11.78 months. Consanguinity was positive in 76.9% of the PID group. Lower respiratory tract infections ,persistent fungal infections and lymphopenia were the most significant warning signs for diagnosing PID with a p value of (0.01). Combined, lower respiratory tract infections, fungal infections and lymphopenia were 12.3 times more likely to be associated with PID. Conclusion: Focused screening in high risk neonates proved to be a valuable tool for diagnosis of PID disorders. DOI: https://dx.doi.org/10.4314/ahs.v19i1.18 Cite as: Galal N, Ohida M, Meshaal S, Abd Elaziz D, I E. Targeted screening for primary immunodeficiency disorders in the neonatal period and early infancy. Afri Health Sci. 2019;19(1). 1449-1459. https://dx.doi. org/10.4314/ahs.v19i1.1

The Cause–Effect Dilemma of Hematologic Changes in COVID-19: One Year after the Start of the Pandemic

COVID-19 is a systemic infection that leads to multisystem affection, including hematological changes. On the other hand, the patients who have certain hematological diseases are more susceptible to COVID-19 infection. The aim of this review is to examine the wide spectrum of hematological changes that are reported to occur due to COVID-19 infection. Most of the studies over the past year mainly show that most of these changes are mainly non-specific, but are of prognostic value. On the other hand, the susceptibility of hematological patients to COVID-19 infection and complications remains questionable. Patients with certain hematological diseases (including malignancy) and those who are treated by aggressive immunosuppressive therapy have shown higher rates of COVID-19 infection and complications. On the other hand, for most of the patients suffering from other chronic hematological conditions, no evidence has shown a greater risk of infection, compared to the general population

Quantum Chaotic Honey Badger Algorithm for Feature Selection

Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria

Quantum Chaotic Honey Badger Algorithm for Feature Selection

Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria

Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering

International audienceThis paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID- 19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 imagesTaikomosios informatikos katedraVytauto Didžiojo universiteta

CD4+ CD25+ cells in type 1 diabetic patients with other autoimmune manifestations

The existence of multiple autoimmune disorders in diabetics may indicate underlying primary defects of immune regulation. The study aims at estimation of defects of CD4+ CD25+high cells among diabetic children with multiple autoimmune manifestations, and identification of disease characteristics in those children. Twenty-two cases with type 1 diabetes associated with other autoimmune diseases were recruited from the Diabetic Endocrine and Metabolic Pediatric Unit (DEMPU), Cairo University along with twenty-one normal subjects matched for age and sex as a control group. Their anthropometric measurements, diabetic profiles and glycemic control were recorded. Laboratory investigations included complete blood picture, glycosylated hemoglobin, antithyroid antibodies, celiac antibody panel and inflammatory bowel disease markers when indicated. Flow cytometric analysis of T-cell subpopulation was performed using anti-CD3, anti-CD4, anti-CD8, anti-CD25 monoclonal antibodies. Three cases revealed a proportion of CD4+ CD25+high below 0.1% and one case had zero counts. However, this observation did not mount to a significant statistical difference between the case and control groups neither in percentage nor absolute numbers. Significant statistical differences were observed between the case and the control groups regarding their height, weight centiles, as well as hemoglobin percentage, white cell counts and the absolute lymphocytic counts. We concluded that, derangements of CD4+ CD25+high cells may exist among diabetic children with multiple autoimmune manifestations indicating defects of immune controllers

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE