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Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early detection mechanism increases the chances of successful treatment and survival, as early diagnosis promptly offers interventions that can dramatically reduce the rate of deaths attributed to this disease. Here, a customized Convolutional Neural Network (CNN) model is proposed for cervical cancerous cell detection. It includes three convolutional layers with increasing filter sizes and max-pooling layers, followed by dropout and dense layers for improved feature extraction and robust learning. By using ResNet models as inspiration, the model further innovates by incorporating skip connections into the CNN design. By enabling direct feature transmission from earlier to later layers, skip links enhance gradient flow and help preserve important spatial information. By boosting feature propagation, this integration increases the model's ability to recognize minute patterns in cervical cell images, hence increasing classification accuracy. In our methodology, the SIPaKMeD dataset has been employed which contains 4049 cervical cell images that are arranged into five different categories. To address class imbalance, Generative Adversarial Networks (GANs) have been applied for data augmentation; that is, synthetic images have been created, that improve the diversity of the dataset and further enhance the robustness of the same. The present model is astonishingly accurate in classifying five cervical cell types: koilocytes, superficial-intermediate, parabasal, dyskeratotic, and metaplastic, thus significantly enhancing early detection and diagnosis of cervical cancer. The model gives an excellent performance because it has a validation accuracy of 99.11% and a training accuracy of 99.82%. It is a reliable model in the diagnosis of cervical cancerous cells because it ensures advancement in the computer-assisted cervical cancer detection system., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance., (© 2024. The Author(s).)

Detection of aerial activities, including drones and birds, has practical implications for automating bird surveys and developing radar systems for aerial object collision detection. Convolutional neural networks (CNNs) have been extensively utilized for image recognition and classification tasks, albeit prior research predominantly focuses on single-class 'drone' classification. However, a gap persists in achieving high accuracy for multi-class classification. To address the limitations of traditional CNNs, such as vanishing gradients and the necessity for numerous layers, this study introduces a novel model termed "MobVGG." This model combines the architectures of MobileNetV2 and VGG16 to accurately classify images as either 'bird' or 'drone'. The dataset comprises 4212 images for each category of 'bird' and 'drone'. The stringent methodology was applied for dataset preparation and model training to ensure the reliability of the findings. Comparative analysis with previous research demonstrates that the proposed MobVGG model, trained on both 'bird' and 'drone' images, achieves superior accuracy (96 %) compared to benchmark studies. Our paper targets researchers and graduate students as its primary audience., Competing Interests: The authors have no conflict of interest., (© 2024 The Authors.)

Background: In today's digital age, various diseases drastically reduce people's quality of life. Arthritis is one amongst the most common and debilitating maladies. Osteoarthritis affects several joints, including the hands, knees, spine, and hips. This study focuses on the medical disorder underlying Knee Osteoarthritis (KOA) which severely impairs people's quality of life. KOA is characterised by restricted mobility, stiffness, and terrible pain and can be caused by a range of factors such as ageing, obesity, and traumas. This degenerative disorder leads to progressive wear and tear of the knee joint., Methods: To combat arthritis in the kneecap, this study employs a 12-layer Convolutional Neural Network (CNN) to reach deep learning capabilities. A collection of data from the Osteoarthritis Initiative (OAI) is used to classify KOA. Through the use of medical image processing; the study ascertains whether an individual has this ailment. A sophisticated CNN architecture created especially for binary classification and KOA severity utilising deep learning algorithms is the main component of this work., Results: The cross-entropy loss function is an important component of the model's laborious design that classifies data into two groups. The remaining section uses the Kellgren-Lawrence (KL) grade to classify the disease's severity. In the binary classification, the proposed algorithm outperforms previous methods with an accuracy rate of 92.3%, and in the multiclassification, its accuracy rate is 78.4% which is superior to the previous findings., Conclusion: Looking ahead, the research broadens the scope of this work by gathering information from various sources and using these methods on a wider range of datasets and situations. The potential for major advancements in the field of osteoarthritis detection and classification is highlighted by this forward-looking approach. Furthermore, this method reduces the intervention of medical practitioners and ultimately results in accurate diagnosis., Clinical Trial Number: Not applicable., (© 2024. The Author(s).)

Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed., (© 2024. The Author(s).)

Medical image security is paramount in the digital era but remains a significant challenge. This paper introduces an innovative zero-watermarking methodology tailored for medical imaging, ensuring robust protection without compromising image quality. We utilize Sped-up Robust features for high-precision feature extraction and singular value decomposition (SVD) to embed watermarks into the frequency domain, preserving the original image's integrity. Our methodology uniquely encodes watermarks in a non-intrusive manner, leveraging the robustness of the extracted features and the resilience of the SVD approach. The embedded watermark is imperceptible, maintaining the diagnostic value of medical images. Extensive experiments under various attacks, including Gaussian noise, JPEG compression, and geometric distortions, demonstrate the methodology's superior performance. The results reveal exceptional robustness, with high Normalized Correlation (NC) and Peak Signal-to-noise ratio (PSNR) values, outperforming existing techniques. Specifically, under Gaussian noise and rotation attacks, the watermark retrieved from the encrypted domain maintained an NC value close to 1.00, signifying near-perfect resilience. Even under severe attacks such as 30% cropping, the methodology exhibited a significantly higher NC compared to current state-of-the-art methods., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Taj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng's Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input., (© 2024. The Author(s).)

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models., Competing Interests: The authors have declared no conflict of interest., (© 2024 The Authors.)

The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking., (© 2024. The Author(s).)

Purpose: FRAX ® is a tool used for evaluation of risk of fracture in RA and non-RA patients and to identify those eligible for intervention. One of the limitations of FRAX in RA settings is that it does not consider factors known to contribute to osteoporosis such as autoantibodies. This study analysed the association of anti-mutated citrullinated vimentin antibody (anti-MCV), anti-cyclic citrullinated peptide antibody (anti-CCP), IgM rheumatoid factor (RF), IgA RF with 10-year risk of major osteoporosis and hip fracture., Methods: FRAX ® tool was used to estimate 10-year risk of major osteoporosis fracture and hip fracture in 189 RA patients over 40 years of age. Anti-MCV, anti-CCP, IgM RF and IgA RF were tested using enzyme immunoassay and analysed at different levels. Results were adjusted for various confounders including disease activity., Results: Fifty-one (26.9%) RA patients had high (≥20%) 10-year risk of major osteoporosis fracture and 67 (35.4%) had high (>3%) 10-year risk of hip fracture. Among all the tested autoantibodies, only IgM RF at elevated levels was associated with high 10-year risk of major osteoporosis fracture (adjusted OR = 4.1, 95% CI = 1.5-11.3, p = 0.006) and of hip fracture (adjusted OR = 17.4, 95% CI = 3.7-81.3, p < 0.0001). There was no agreement between FRAX and femoral neck (FN) BMD. None of the autoantibodies tested were associated with FN osteopenia or osteoporosis including IgM RF at high levels., Conclusion: Our study highlights the importance of quantitative measurement of autoantibodies in assessment of risk for fractures among RA patients. Our preliminary findings need to be assessed in prospective studies to determine the actual predictive value of high IgM RF levels among patients with RA., Competing Interests: The authors declare that they have no competing interests in this work., (© 2024 Sghiri et al.)

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Background: Alcoholism is a catastrophic condition that causes brain damage as well as neurological, social, and behavioral difficulties., Limitations: This illness is often assessed using the Cut down, Annoyed, Guilty, and Eye-opener examination technique, which assesses the intensity of an alcohol problem. This technique is protracted, arduous, error-prone, and errant., Method: As a result, the intention of this paper is to design a cutting-edge system for automatically identifying alcoholism utilizing electroencephalography (EEG) signals, that can alleviate these problems and aid practitioners and investigators. First, we investigate the feasibility of using the Fast Walsh-Hadamard transform of EEG signals to explore the unpredictable essence and variability of EEG indicators in the suggested framework. Second, thirty-six linear and nonlinear features for deciphering the dynamic pattern of healthy and alcoholic EEG signals are discovered. Subsequently, we suggested a strategy for selecting powerful features. Finally, nineteen machine learning algorithms and five neural network classifiers are used to assess the overall performance of selected attributes., Results: The extensive experiments show that the suggested method provides the best classification efficiency, with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the features chosen using the correlation-based FS approach with Recurrent Neural Networks. With recently introduced matrix determinant features, a classification accuracy of 93.3% is also attained. Moreover, we developed a novel index that uses clinically meaningful features to differentiate between healthy and alcoholic categories with a unique integer. This index can assist health care workers, commercial companies, and design engineers in developing a real-time system with 100% classification results for the computerized framework., Competing Interests: Conflict of interestAuthors declared there is no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: : The prevalence of celiac disease (CD) is relatively high in Saudi Arabia, and little is known about the accuracy of serological markers in the local population. This study aimed to assess the diagnostic performance of various serological markers for detecting CD in Saudi children and adults., Methods: We conducted a retrospective study of 148 CD patients and 512 controls to assess the diagnostic performances of IgA anti-tissue transglutaminase antibodies (TTG), IgG anti-TTG, IgA anti-deamidated gliadin peptide antibodies (anti-DGP), IgG anti-DGP, and endomysium antibodies (EMA)., Results: : Immunoglobulin A (IgA) anti-TTG was the most sensitive test [98.9% (95% confidence interval (CI) 94.1-99.8%)], while EMA was the most specific [100%, 95%CI 98.6-100%]. By applying the criteria of IgA anti-TTG titers ≥10 × upper limit of normal (ULN) and positive EMA, 57.3% of patients could have avoided intestinal biopsy. IgG anti-DGP test had a sensitivity of 85.9% (95% CI = 77.3-91.5%) and a specificity of 93.5% (95% CI = (90.0-95.9%). Titers of IgA anti-TTG, IgA anti-DGP, and IgG anti-DGP were higher in CD patients with the Marsh 3c class than in those with the Marsh 3b and Marsh 3a classes. IgG anti-TTG and IgA anti-DGP had no additional diagnostic value., Conclusions: : IgA anti-TTG and EMA are excellent CD markers in children and adults. The use of IgA anti-TTG titers ≥10 × ULN and positive EMA as criteria for CD diagnosis in children and adults might be a good alternative to intestinal biopsy., Competing Interests: None

Objectives: Little is known about genes predisposing to systemic bone loss (SBL) in rheumatoid arthritis (RA). Therefore, we examined the association between SBL and variants of genes playing a critical role in both immune response and bone homeostasis among patients with RA., Methods: IRAK-1 rs3027898, IRAK-2 rs3844283, IRAK-2 rs708035, IFIH1 rs1990760, CD40 rs48104850, TNFAIP3 rs2230926, and miR146-a rs2910164 were genotyped in 176 adult RA patients. Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA)., Results: Low BMD was observed in 116 (65.9%) patients. Among them, 60 (34.1%) had low femoral neck (FN) Z score, 72 (40.9%) had low total femur (TF) Z score, and 105 (59.6%) had low lumbar spine (LS) Z score. Among all the SNPs assessed, only CD40 rs4810485 was found to be associated with reduced TF Z score with the CD40 rs4810485 T allele protecting against reduced TF Z score (OR = 0.40, 95% CI = 0.23-0.68, p = 0.0005). This association was confirmed in the multivariate logistic regression analysis (OR = 0.31, 95% CI = 0.16-0.59, p = 3.84 × 10 -4 ). Moreover, median FN BMD was reduced among RA patients with CD40 rs4810485 GG genotype compared to RA patients harbouring CD40 rs4810485 TT and GT genotypes (0.788 ± 0.136 versus 0.826 ± 0.146 g/cm 2 , p = 0.001). IRAK-1 rs3027898, IRAK-2 rs3844283, rs708035, IFIH rs1990760, TNFAIP3 rs2230926, and miR146-a rs2910164 were not found to be associated with SBL., Conclusion: This study for the first time ever demonstrated an association between a CD40 genetic variant and SBL among patients with RA., Key Points: • CD40 rs4810485 GG genotype is associated with decreased BMD among patients with RA. • CD40 rs4810485 might serve as a genetic marker for SBL in RA. • CD40 genetic variations might be integrated in future development of more effective therapeutic interventions for prevention of SBL in RA., (© 2022. International League of Associations for Rheumatology (ILAR).)

Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to realizing automated, robust brain-computer interface (BCI) systems. In the present study, we proposed a pretrained convolutional neural network (CNN)-based new automated framework feasible for robust BCI systems with small and ample samples of motor and mental imagery EEG training data. The framework is explored by investigating the implications of different limiting factors, such as learning rates and optimizers, processed versus unprocessed scalograms, and features derived from untuned pretrained models in small, medium, and large pretrained CNN models. The experiments were performed on three public datasets obtained from BCI Competition III. The datasets were denoised with multiscale principal component analysis, and time-frequency scalograms were obtained by employing a continuous wavelet transform. The scalograms were fed into several variants of ten pretrained models for feature extraction and identification of different EEG tasks. The experimental results showed that ShuffleNet yielded the maximum average classification accuracy of 99.52% using an RMSProp optimizer with a learning rate of 0.000 1. It was observed that low learning rates converge to more optimal performances compared to high learning rates. Moreover, noisy scalograms and features extracted from untuned networks resulted in slightly lower performance than denoised scalograms and tuned networks, respectively. The overall results suggest that pretrained models are robust when identifying EEG signals because of their ability to preserve the time-frequency structure of EEG signals and promising classification outcomes., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Despite the benefits of smart grids, concerns about security and privacy arise when a large number of heterogeneous devices communicate via a public network. A novel privacy-preserving method for smart grid-based home area networks (HAN) is proposed in this research. To aggregate data from diverse household appliances, the proposed approach uses homomorphic Paillier encryption, Chinese remainder theorem, and one-way hash function. The privacy in Internet of things (IoT)-enabled smart homes is one of the major concerns of the research community. In the proposed scheme, the sink node not only aggregates the data but also enables the early detection of false data injection and replay attacks. According to the security analysis, the proposed approach offers adequate security. The smart grid distributes power and facilitates a two-way communications channel that leads to transparency and developing trust.

In many smart devices and numerous digital applications, authentication mechanisms are widely used to validate the legitimacy of users' identification. As a result of the increased use of mobile devices, most people tend to save sensitive and secret information over such devices. Personal Identification Number (PIN)-based and alphanumeric passwords are simple to remember, but at the same time, they are vulnerable to hackers. Being difficult to guess and more user-friendly, graphical passwords have grown in popularity as an alternative to all such textual passwords. This paper describes an innovative, hybrid, and much more robust user authentication approach, named GRA-PIN (GRAphical and PIN-based), which combines the merits of both graphical and pin-based techniques. The feature of simple arithmetic operations (addition and subtraction) is incorporated in the proposed scheme, through which random passwords are generated for each login attempt. In the study, we have conducted a comparative study between the GRA-PIN scheme with existing PIN-based and pattern-based (swipe-based) authentications approaches using the standard Software Usability Scale (SUS). The usability score of GRA-PIN was analyzed to be as high as 94%, indicating that it is more reliable and user friendly. Furthermore, the security of the proposed scheme was challenged through an experiment wherein three different attackers, having a complete understanding of the proposed scheme, attempted to crack the technique via shoulder surfing, guessing, and camera attack, but they were unsuccessful.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network's lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

Introduction: Autoantibodies such as IgM rheumatoid factor (RF) and anti-citrullinated proteins/peptides antibodies (ACPA) have previously been incriminated in systemic bone loss in rheumatoid arthritis (RA). There are, however, no data describing association of IgA RF and IgG RF with systemic bone loss., Objective: This study was aimed to investigate the association of RF isotypes with systemic bone loss among patients with RA., Methods: RF isotypes and ACPA were measured by enzyme-linked immunosorbent assay among 153 patients with RA. Bone mineral density (BMD) was assessed using dual-energy X-ray absorptiometry., Results: Ninety-four (61.4%) patients had positive IgA RF, 89 (58.2%) had positive IgG RF, 109 (71.2%) had positive IgM RF, whereas 122 (80.3%) RA patients tested positive for ACPA. Compared to the IgA RF-negative patients, IgA RF-positive patients exhibited higher disease activity and had higher RF titers. Seven (4.6%) patients had low BMD at femoral neck, 12 (7.8%) at total femur, and 47 (30.7%) at lumbar spine. IgA RF was found to be associated with protection against low BMD at spine (OR = 0.47, 95% CI = 0.23-0.95, p = 0.034). This association was further confirmed in the multivariate regression analysis taking into account several potential confounding factors (OR = 0.21, 95% CI = 0.06-0.65, p = 0.039). No association between low BMD and the presence of IgG RF or IgM RF or ACPA was found., Conclusion: IgA RF for the first time ever was shown to be associated with BMD preservation at spine in RA. Key points • IgA RF was associated with protection against low spinal BMD. • No association between low BMD and the presence of IgG RF or IgM RF was found., (© 2021. International League of Associations for Rheumatology (ILAR).)

Vehicular Ad hoc Network (VANET) is a modern concept that enables network nodes to communicate and disseminate information. VANET is a heterogeneous network, due to which the VANET environment exposes to have various security and privacy challenges. In the future, the automobile industry will progress towards assembling electric vehicles containing energy storage batteries employing these resources to travel as an alternative to gasoline/petroleum. These vehicles may have the capability to share their energy resources upon the request of vehicles having limited energy resources. In this article, we have proposed a trust management-based secure energy sharing mechanism, named vTrust, which computes the trust degree of nodes to authenticate nodes. The proposed mechanism is a multi-leveled centralized approach utilizing both the infrastructure and vehicles to sustain a secure environment. The proposed vTrust can aggregate and propagate the degree of trust to enhance scalability. The node that requests to obtain the energy resources may have to maintain a specified level of trust threshold for earning resources. We have also evaluated the performance of the proposed mechanism against several existing approaches and determine that the proposed mechanism can efficiently manage a secure environment during resource sharing by maintaining average malicious nodes detection of 91.3% and average successful energy sharing rate of 89.5%, which is significantly higher in comparison to the existing approaches.

Opportunistic routing is an emerging routing technology that was proposed to overcome the drawback of unreliable transmission, especially in Wireless Sensor Networks (WSNs). Over the years, many forwarder methods were proposed to improve the performance in opportunistic routing. However, based on existing works, the findings have shown that there is still room for improvement in this domain, especially in the aspects of latency, network lifetime, and packet delivery ratio. In this work, a new relay node selection method was proposed. The proposed method used the minimum or maximum range and optimum energy level to select the best relay node to forward packets to improve the performance in opportunistic routing. OMNeT++ and MiXiM framework were used to simulate and evaluate the proposed method. The simulation settings were adopted based on the benchmark scheme. The evaluation results showed that our proposed method outperforms in the aspect of latency, network lifetime, and packet delivery ratio as compared to the benchmark scheme., Competing Interests: The authors declare there are no competing interests., (©2020 Yee et al.)

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this work., (Copyright © 2020 Nighat Bibi et al.)

Recently, many platforms have outsourced tasks to numerous smartphone devices known as Mobile Crowd-sourcing System (MCS). The data is collected and transferred to the platform for further analysis and processing. These data needs to maintain confidentiality while moving from smartphones to the platform. Moreover, the limitations of computation resources in smartphones need to be addressed to balance the confidentiality of the data and the capabilities of the devices. For this reason, elliptic curve cryptography (ECC) is accepted, widespread, and suitable for use in limited resources environments such as smartphone devices. ECC reduces energy consumption and maximizes devices' efficiency by using small crypto keys with the same strength of the required cryptography of other cryptosystems. Thus, ECC is the preferred approach for many environments, including the MCS, Internet of Things (IoT) and wireless sensor networks (WSNs). Many implementations of ECC increase the process of encryption and/or increase the space overhead by, for instance, incorrectly mapping points to EC with extra padding bits. Moreover, the wrong mapping method used in ECC results in increasing the computation efforts. This study provides comprehensive details about the mapping techniques used in the ECC mapping phase, and presents performance results about widely used elliptic curves. In addition, it suggests an optimal enhanced mapping method and size of padding bit to secure communications that guarantee the successful mapping of points to EC and reduce the size of padding bits.

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

Recent growth in the Internet of Things (IoT) has raised security concerns over the confidentiality of data exchanged between IoT devices and the edge. Many IoT systems adopt asymmetric cryptography to secure their data and communications. A drawback of asymmetric cryptography is the sizeable computation and space requirements. However, elliptic curve cryptography (ECC) is widely used in constrained environments for asymmetric cryptography due its superiority in generating a powerful encryption mechanism with small key sizes. ECC increases device performance and lowers power consumption, meaning it is suitable for diverse applications ranging from the IoT to wireless sensor network (WSN) devices. To ensure the confidentiality and security of data and communications, it is necessary to implement ECC robustly. A special area of focus in this regard is the mapping phase. This study's objective was to propose a tested and trusted scheme that offers authenticated encryption (AE) via enhancing the mapping phase of a plain text to an elliptic curve to resist several encryption attacks such as Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA). The proposed scheme also undertakes evaluation and analysis related to security requirements for specific encryption attributes. Finally, results from a comparison of the proposed scheme and other schemes are presented, evaluating each one's security characteristics and performance measurements. Our scheme is efficient in a way that makes so suitable to the IoT, and in particular to the Industrial IoT and the new Urbanization where the demands for services are huge.

Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities.

Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.

Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.

The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d'Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.

Objectives: This study was aimed to investigate the association of the single nucleotide polymorphism of tumor necrosis factor receptor associated factor 6 (TRAF6), rs540386, with low bone mineral density (BMD) among patients with rheumatoid arthritis (RA)., Methods: TRAF6 rs540386 genotyping was performed by mutagenically separated PCR in a cohort of 188 (23 men, 165 women, median age, 56.2 years) adult RA patients and 224 age and gender-matched controls. BMD was measured using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy advance scans, GE Healthcare, USA)., Results: Among the RA patients, 64 (55 women, 9 men) had low BMD comprising of 57 patients with osteoporosis and 7 with osteopenia. Whereas TRAF6 rs540386 was not associated with RA susceptibility, it was however found to be a risk factor for reduced lumbar spine Z-score in the recessive model (OR = 3.34, 95% CI = (1.01-11.00), p = 0.038). This association was confirmed further in the multivariate logistic regression analysis taking into account several potential confounding factors (OR = 3.34 (1.01-11.00), p = 0.048). In addition, mean total femur Z-score was found to be reduced in TT patients when compared to CC + CT patients (- 1.30 ± 1.32 versus - 0.60 ± 1.05, p = 0.034). No association between TRAF6 rs540386 and local bone damage was observed., Conclusions: This study for the first time ever demonstrated an association between a genetic variant of TRAF6 and low BMD among patients with RA. Further investigations are needed to elucidate the exact role of this variant.

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

This study was performed to investigate the association of the single nucleotide polymorphisms of interleukin-1 receptor-associated kinase 2 (IRAK2) rs3844283 and rs708035 with rheumatoid arthritis (RA). IRAK2 rs3844283 and rs708035genotyping was determined by mutagenically separated PCR with specifically designed primers in a cohort of 222 (30 men, 192 women, mean age 49 years) adult RA patients and 224 matched controls. IRAK2 rs3844283 C allele was detected in 66% of RA patients and 74% of controls. The CC genotype was the most frequent genotype in both RA patients (45.5%) and the controls (56.3%). The G allele was found to be associated with RA susceptibility (OR = 1.47, 95% CI = 1.10-1.96, p = 0.008). The GG genotype was found to be associated with RA in the co-dominant and the dominant models (OR = 2.03, 95% CI = 1.08-3.81, p = 0.042 and OR = 1.54, 95% CI = 1.06-2.23, p = 0.023, respectively). IRAK2 rs708035 was found not to be in the Hardy-Weinberg equilibrium. The hyperfunctional IRAK2 rs708035 A allele was more frequent in RA patients than in controls (69.9 versus 62.2%, respectively, p = 0.015). Moreover, IRAK2 rs708035 and IRAK2 rs3844283 were in linkage disequilibrium and the GA haplotype was significantly more frequent in RA patients than in controls (p = 0.034). This study for the first time ever reports the association of IRAK2 rs3844283, IRAK2 rs708035, and the corresponding haplotypes with RA. Functional studies are recommended to elucidate the risk posed by the GA haplotype for the development of RA.

In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved Hydrocast (Co-improved Hydrocast). In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR) of the received signal is not within the predefined threshold then the maximal ratio combining (MRC) is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast.

In this paper, we propose two schemes; position-aware mobility pattern (PAMP) and cooperative PAMP (Co PAMP). The first one is an optimization scheme that avoids void hole occurrence and minimizes the uncertainty in the position estimation of glider's. The second one is a cooperative routing scheme that reduces the packet drop ratio by using the relay cooperation. Both techniques use gliders that stay at sojourn positions for a predefined time, at sojourn position self-confidence (s-confidence) and neighbor-confidence (n-confidence) regions that are estimated for balanced energy consumption. The transmission power of a glider is adjusted according to those confidence regions. Simulation results show that our proposed schemes outperform the compared existing one in terms of packet delivery ratio, void zones and energy consumption.

Background: Current protocols for detection of circulating fibrocytes (CFs) in peripheral blood described in various pulmonary and nonpulmonary disorders involve complex and time consuming, non standardized techniques., Objective: Testing a method to rapidly detect and quantify CFs using whole blood lysis flow cytometry-based assay in patients with idiopathic pulmonary fibrosis (IPF) and healthy controls., Methods: One milliliter of venous blood sample in ethylenediaminetetraacetic acid (EDTA) from 33 IPF patients and 35 healthy control subjects was collected. Using whole blood lysis method peripheral blood leukocytes were labeled with monoclonal antibodies for cell surface (CD34 and CD45) and intracellular markers (collagen-1) for flow cytometric analysis. CFs were defined as CD45(+) cells coexpressing collagen-I and CD34 molecules., Results: In 29 (87.8%) IPF patients and 10 (28.5%) control subjects, a well-defined highly granular CD45(+) cell population was detected in dot plots generated by side scatter properties of CD45(+) cells. These CD45(+) cells were identified as CFs on the basis of coexpression of collagen-I and CD34; none of the other cell types in the peripheral blood were labeled with these monoclonal antibodies. In IPF patients the percentage of CFs was significantly higher compared to healthy controls (median (range): 1.37% (0.52-5.65) and 1.04% (0.1-1.84), respectively; P = 0.03)., Conclusions: Whole blood lysis method combined with fluorescence-activated cell sorting (FACS) allows detecting a well-defined homogeneous population of CFs. This method is simple, reproducible, and provides an accurate and rapid estimation of CFs.

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

Background: Allergy to hen's egg and meat contributes significantly to the manifestations of food allergy all over the world., Objectives: This study was performed to assess the presence of hen's egg and meat specific IgE antibodies among patients investigated for various allergic disorders., Methods: This is a retrospective study performed at King Khalid University Hosptial, Riyadh. Data from 421 patients with allergic disorders screened for food specific IgE antibodies between January 2009 and March 2011 were analyzed. Sixty (14.25%) patients including 42 males and 18 females with the mean age (sd) of 7.5 (7.4) years were found to have specific IgE antibodies against hen's egg and chicken meat. There were 56 (93.3%) children and 4 (6.7%) adult patients. Specific IgE antibodies were measured by radioallergosorbent test (RAST) using Pharmacia ImmunoCAP 250 analyzer., Results: Atopic dermatitis was the most common (55%) clinical condition. Out of the total 60 patients harboring hen's egg and chicken meat specific IgE antibodies high levels of egg white, yolk and chicken meat specific IgEs were detected in 58 (96.6%), 37 (61.6%) and 6 (10%) patients respectively. Both the egg white and yolk antibodies coexisted in 35 (58.3%) patients., Conclusion: Sensitization against hen's egg was higher compared to the chicken meat. Egg white sensitization higher than the egg yolk particularly in Saudi children with food related allergic disorders.

Background: Systemic lupus erythematosus (SLE) is a disease with diverse clinical presentations due to interaction between genetic and environmental factors. SLE is associated worldwide with polymorphisms at various loci, including the major histocompatibility complex (MHC), although inconsistencies exist among these studies., Aims: This study was carried out to investigate, the association of HLA-DRB1, DRB3, DRB4, DRB5, and DQB1 alleles in SLE patients and clinical presentations at Qassim, Saudi Arabia., Methods: Fifty one patients with SLE-84.3% of whom had kidney involvement were studied in a case control study for HLA-DRB1, DRB3, DRB4, DRB5, and DQB1., Results: It was found that DRB3 is a protective gene among Saudi's against SLE, HLA DRB3, HLA DRB1*11 frequency was increased in patients with serositis with a p value of (0.004), (0.047) respectively, increased frequency of HLA DQB1*3 among SLE patients with skin manifestations with a p value of (0.041), the frequency of HLA DRB1*15 alleles was increased among SLE patients with nephritis with a p value of (0.029), the frequency of HLA DRB1*11 among those with hematological manifestations with a p value of (0.03) and the frequency DRB1*10 was found to be increased among SLE patients with neurological manifestations with a p value of (0.002)., Conclusion: In contradistinction to what have been found among other populations DRB3 is a protective gene among Saudi's against SLE. No evidence for a role of the HLA-DRB1, DRB4, DRB5, DQB1 alleles. There was an increased HLA DRB3 frequency with serositis, DQB1*3 skin manifestations, HLA DRB1*15 with nephritis, DRB1*10 with hematological manifestations and DRB1*11 with neurological manifestations.

Background: Anti-nuclear antibody (ANA) test as the first level investigation for detection of auto-immune rheumatic disease has been recommended in a number of international guidelines. This study was performed to evaluate the local practice and trends of auto-antibody laboratory requests., Methods: Data were collected from 249 initial laboratory requests for first level auto-antibody detection between April 2012 and April 2013 in the Immunology Unit at King Khalid University Hospital, Riyadh. This group of patients included 151 females and 98 males (mean age 40.1 +/- 21; range 4-85 years)., Results: Of the total requests, ANA as a single first level investigation was requested by only 32 (13%) clinicians whereas the rest of the investigations included simultaneous testing of ANA and second level extractable nuclear antigen (ENA) auto-antibody tests. Anti-double stranded DNA (anti-dsDNA) antibody was simultaneously tested with ANA in 158 patients as first level test where both the tests were positive in 44 (27.8%) patients and in 24 (15.1%) patients a negative ANA test was associated with a positive anti-dsDNA antibody test. Rheumatoid factor (RF) tested positive in 04/53 (7.5%), anti-neutrophil cytoplasmic antibody (ANCA) in 01/48 (2%) and SS-A and SS-B in 03/37 (8.1%) requests as first level tests with ANA., Conclusions: Using second level auto-antibody tests in conjunction with ANA as the first line investigation does not appear to be a cost effective approach, highlighting the importance of adherence to the guidelines. ANA negative and anti-dsDNA positive group of patients requires further assessment in a large scale study.

Objective: To characterize, quantify, and assess the function of colostral secretory immunoglobulin A (sIgA) in Saudi women., Methods: This prospective study was performed between March 2009 and February 2010 at King Khalid University Hospital, Riyadh, Saudi Arabia. Twenty milliliters of colostrum sample was collected from each of 23 healthy Saudi women (mean age 26+/-4 years) included in the study within 48 hours postpartum. Levels of sIgA and sIgM were determined by enzyme-linked immunosorbent assay (ELISA) and purification of secretory antibodies from pooled clarified sample was performed by thiophilic-gel chromatography, Jacalin-agarose chromatography, and Sephacryl S-300 gel filtration. Antibody induced respiratory burst in peripheral blood neutrophils and monocytes was assessed by chemiluminescence., Results: The median concentration of sIgA1 was 0.053 mg/ml, sIgA2 0.047 mg/ml and sIgM 0.067 mg/ml with interquartile ranges of 0.308, 0.158 and 0.150. The levels of antibodies were no different. Whereas, 60% of IgA1 was present in dimeric and 30% in trimeric form; the major bulk of sIgA2 (85%) were comprised of the dimeric form. Both sIgA and serum IgA were able to induce effective and almost identical respiratory bursts in neutrophils and monocytes., Conclusion: Dimeric forms of sIgA were the predominant antibodies in colostrum samples and sIgA antibodies exhibited functional similarity with serum IgA.

Background: Detection of specific IgE antibodies against food materials indicates allergic sensitization. Some very widely consumed foods materials such as garlic and onion have rarely been investigated for their allergenic potential., Objectives: To assess the presence of garlic and onion specific IgE antibodies in patients investigated for food allergy., Methods: Radioallergosorbent test (RAST) results of 108 patients with clinical suspicion of food allergy who were specifically screened for garlic and onion specific IgE antibodies along with other food allergens were analyzed retrospectively at King Khalid University Hospital between January 2008 and April 2009. This group of patients included 73 males and 35 females with mean age 27+13.2 years. Estimation of garlic and onion specific IgE antibodies was performed by radioallergosorbent test (RAST) using Pharmacia ImmunoCAP 250 analyzer., Results: Out of the 108 patients 15 (13.8%) had garlic and onion specific IgE antibodies in their sera. Garlic specific IgE antibodies with the RAST scores between one to four were present in 14 and onion specific IgE were detected in 13 patients. For garlic specific IgEs majority of patients (08) had RAST score of one (0.35-0.69 kU/L) and for onion specific IgE antibodies seven patients had RAST score of two (0.70-3.49 kU/L). Among these patients 12 (80%) were found to have coexisting specific IgE antibodies against garlic and onion., Conclusion: The presence of garlic and onion specific IgE antibodies in a sizeable number of patients indicate sensitization and allergenic potential of these food materials.

Background and Objectives: Infection due to Coxiella burnetii (C burnetii), the causative agent of Q fever is rarely sought for in clinical practice. This study was performed to detect C burnetii infection in patients with pyrexia of undetermined cause (PUC)., Design and Settings: This is a prospective study conducted at King Khalid University Hospital, Riyadh be.tween March 2011 and January 2013., Patients and Methods: A total of 3 mL venous blood was collected from 51 patients with PUC at King Khalid University Hospital, Riyadh. This group of patients included 30 males and 21 females (mean age 33.9 [21.3] years) with the history of febrile illness ranging between 4 and 8 weeks. A control group of 50 healthy individuals comprising 39 males and 11 females (mean age 27 [9] years) was also included in the study. Detection of phase II C burnetii-specific IgG antibodies was performed by immunofluorescence assay, and a titer of > 1:64 was considered positive., Results: Phase II C burnetii-specific IgG antibodies were detected in 18 (35.2%) patients out of the total 51 tested. Two (4%) individuals out of 50 in the control group tested positive for anti-C burnetii IgG antibodies. The proportion of positive results among the patients was significantly higher than the controls (P < .0002, 95% CI, 15.09-46.25). The antibody titer range was between 1:128 and 1:1024 where 6 patients had titers of 1:256, 5 had 1:512, 4 had 1024, and 3 had 1:128., Conclusion: The evidence of C burnetii infection in a sizable number of patients emphasizes the need for inclusion of serologic investigations for Q fever in patients with PUC.

Background: Studies have demonstrated associations between cytokine gene polymorphisms and the risk of idiopathic pulmonary fibrosis (IPF). We therefore examined polymorphisms in the genes encoding interleukin (IL)-6, IL-10, interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), and transforming growth factor-beta 1 (TGF-β1), and compared the serum levels of these cytokines in IPF patients and healthy controls. Furthermore, we examined the association of the studied genotypes and serum cytokine levels with physiological parameters and the extent of parenchymal involvement determined by high-resolution computed tomography (HRCT)., Methods: Sixty patients with IPF and 150 healthy controls were included. Cytokine genotyping was performed using the polymerase chain reaction sequence specific primer (PCR-SSP) method. In a subset of patients and controls, serum cytokine levels were determined by enzyme-linked immunosorbent assay., Results: There was no difference between IPF patients and controls in the genotype and allele distributions of polymorphisms in TNF-α, IFN-γ, IL-6, IL-10, and TGF-β1 (all p > 0.05). The TNF-α (-308) GG, IL-6 (-174) GG and CG, and IL-10 (-1082, -819, -592) ACC ATA genotypes were significantly associated with HRCT scores (all p < 0.05). IL-10 (-1082, -819, -592) ACC haplotype was associated with the diffusion capacity of the lung for carbon monoxide, and ATA haplotype was associated with the partial pressure of oxygen (PaO2) (all p < 0.05). The TGF-β1 (codons 10 and 25) TC GG, TC GC, CC GG and CC GC genotypes were significantly associated with the PaO2 and HRCT scores (p < 0.05). The TGF-β1 (codons 10 and 25) CC GG genotype (5 patients) was significantly associated with higher PaO2 value and less parenchymal involvement (i.e., a lower total extent score) compared to the other TGF-β1 genotypes (81.5 ± 11.8 mm Hg vs. 67.4 ± 11.1 mm Hg, p = 0.009 and 5.60 ± 1.3 vs. 8.51 ± 2.9, p = 0.037, respectively). Significant differences were noted between patients (n = 38) and controls (n = 36) in the serum levels of IL-6 and IL-10 (both, p < 0.0001), but not in the levels of TNF-α and TGF-β1 (both, p > 0.05)., Conclusion: The studied genotypes and alleles do not predispose to the development of IPF but appear to play an important role in disease severity. Our results suggest that the TGF-β1 (codons 10 and 25) CC GG genotype could be a useful genetic marker for identifying a subset of IPF patients with a favorable prognosis; however, validation in a larger sample is required.

Studies have demonstrated associations between cytokine gene polymorphisms and ethnicity. In the present work the authors examine polymorphisms in the genes encoding interleukin-6 (IL-6), IL-10, interferon-gamma (IFNgamma), tumour necrosis factor-alpha (TNFalpha) and transforming growth factor-beta (TGFbeta1) using the polymerase chain reaction sequence-specific primer (PCR-SSP) method in 150 healthy unrelated Saudis, and results compared with those from other studied populations. The genotype distributions were consistent with the Hardy-Weinberg equilibrium, and the genotype frequencies observed among Saudis showed both similarity and difference to other populations. The most notable difference was in the distribution of IL-6, where the Saudi population showed a lower CG genotype frequency compared with White American (22% vs. 39.2%, P = 0.004), Italian (22% vs. 50%, P < 0.0001) and Brazilian (22% vs. 40.8%, P < 0.0001) populations. The study population also showed a higher frequency of the IL-6 GG genotype compared with White Americans (72% vs. 45.1%, P < 0.0001), Italians (72% vs. 41%, P < 0.0001) and Brazilians (72% vs. 49.3%, P < 0.0001). These results may have significant clinical relevance to the understanding of prevalent diseases in Saudi Arabia.

Background and Objectives: Contact allergy is associated with a significant morbidity all over the world. This study was performed to investigate the pattern of sensitization by contact allergens in the local population., Design and Setting: Retrospective study to investigate patch test reactivity among patients with clinical diagnosis of contact dermatitis who were referred to the allergy clinic at the King Khalid University Hospital, Riyadh, between April 2008 and March 2010., Patients and Methods: Of the 196 patients referred to the allergy clinic over the 2-year period, 91 (46.4%) patients reacted to one or more patch test allergens, and these patients were included in this study. The study group included 82 (91.1%) of Saudi nationality and 9 (8.9%) patients of other nationalities. The patch test was performed using the T.R.U.E TEST, containing 24 allergens/allergen mixes., Results: Of the 91 cases who reacted positively to one or more allergens, 67 (73.6%) were females with a mean age of 37 (8.3 years) and 24 (26.4%) were males with a mean age of 34 (11.6 years). Thirty-three (36.2%) patients reacted to nickel sulfate, 14 (15.3%) to p-phenylenediamine, 13 (14.2%) to p-tert-butylphenol-formaldehyde resin, 13 (14.2%) to thimerosal, and 9 (9.8%) to colophony. Reactivity against the rest of the allergens was not remarkable. A significantly higher percentage of females reacted to nickel sulfate (84.8% vs 15.2% in males;P=.0001), p-tert-butylphenol-formaldehyde resin (92.3% vs 7.7%; P=.0001), and thimerosal (76.9% vs 23.1%;P=.03)., Conclusions: Patch test reactivity to nickel sulfate was high. The pattern of contact allergy observed in this study indicates the need for large-scale investigations to identify local allergens responsible for contact allergy and for formulation of policies directed towards avoidance of exposure.

Thomsen-Friedenreich antigen (TF-Ag) is the disaccharide (Gal beta1-3 GalNAc alpha), which is also known as the core 1 structure. The presence of this disaccharide on the surface of approximately 90 percent of carcinomas is due to altered glycosylation in these tumors. TF-Ag plays a role in the adhesive properties of tumor cells involved in metastasis. Treatment of mice with JAA-F11, a monoclonal antibody to TF-Ag alpha inhibited lung metastasis and improved prognosis in a mouse breast cancer model. The presence of naturally occurring antibodies to TF-Ag in cancer patients is related to improved prognosis. The pancarcinoma expression of TF-Ag, combined with the evidence of a mechanistic role for TF-Ag in cancer spread, show that this target would have clinical utility. The presence of naturally occurring antibody to TF-Ag indicates that increasing the anti-TF-Ag antibody would be safe for the cancer patient and indicates that tolerance would not have to be broken to create this immune response. Finally, the prognostic improvements seen clinically and in animal models indicate that this is an important vaccine target.

Most patients with chronic renal failure develop end-stage renal disease (ESRD) that requires renal transplantation. This study investigates the possible associations between human leucocyte antigen (HLA) Class I and Class II molecules with ESRD. Genotyping data (HLA) obtained between 2005 and 2009 on 235 unrelated Saudi patients (147 males, 88 females; mean age: 58 +/- 7 years) with ESRD awaiting renal transplantation were assessed retrospectively at the King Khalid University Hospital. Data were compared with the results on 60 normal, healthy, unrelated Saudi individuals (37 males and 23 females; mean age: 51 +/- 5 years). HLA Class I and Class II antigens were detected by lymphocytotoxicity and a polymerase chain reaction (PCR) method using DNA sequence-specific primers. Although present in small numbers, HLA Cw2 was found in significantly fewer patients (n = 11; 4.68%) compared to normal subjects (n = 9; 15%) and was found to confer protection against ESRD (P = 0.005; relative risk [RR]: 3.594, 95% confidence interval [CI]: 1.415-9.126). Among the HLA Class II antigens, HLA DQB1*03(8) was detected more frequently in the patient group (n = 65; 27.6%) than in the normal controls (n = 9; 15%) and was positively associated with risk of ESRD (P = 0.04; RR: 0.462, 95% CI: 0.215-0.991). No significant differences were observed between the two groups in respect of HLA-A2, HLA-B50(21), HLA-B51(5) and HLA-Cw7 (HLA Class I), and HLA-DRB1*04, HLA-DRB1*07 and HLA-DQB1*02 (HLA Class II). Occurrence of the most frequent HLA alleles was no different between the ESRD group and the controls. The protective role of HLA-Cw2 and the marginal susceptibility associated with HLA-DQBI*03(8) for ESRD requires further investigation.

Reliability of the Widal tube agglutination test has been the subject of many controversies over the years. This study was performed to assess the effect of certain modifications on the performance of Widal test in a novel microplate assay. Sera from 37 patients (21 males; 16 females) (mean age 28 +/- 7 years) were tested in the Immunology Unit at King Khalid University Hospital, Riyadh. Among them were 26 patients with suspected typhoid fever and 11 had bacteriologically confirmed diagnosis of Salmonella infection. The modifications included either the use of 0.5% bovine serum albumin (BSA), absorption of sera with sheep red blood cells (SRBC) or heat inactivation of sera. Compared with Widal tube agglutination test, microplate assay with SRBC absorption of the sera from patients with suspected typhoid fever was not only associated with enhancement of detection titers for both H (p < or = 0.001) and O (p < or = 0.005) Salmonella agglutinins but also the percentage of reactivity. The presence of BSA augmented detection titers for Salmonella H agglutinins (p < or = 0.02) only. Heat inactivation of sera however was found to be associated with reduction in the detectable titers for both H (p < or = 0.03) and O (p < or = 0.01) agglutinins. Increased titers of Salmonella agglutinins were also evident in 11 patients with confirmed diagnosis of Salmonella infection. The novel microplate agglutination assay using the SRBC absorption was associated with enhancement in Widal test reactivity and appears to be a useful alternative for the diagnosis of Salmonella infection.

Tumor-associated carbohydrate antigens (TACA) result from the aberrant glycosylation that is seen with transformation to a tumor cell. The carbohydrate antigens that have been found to be tumor-associated include the mucin related Tn, Sialyl Tn, and Thomsen-Friedenreich antigens, the blood group Lewis related Lewis(Y), Sialyl Lewis(X) and Sialyl Lewis(A), and Lewis(X) (also known as stage-specific embryonic antigen-1, SSEA-1), the glycosphingolipids Globo H and stage-specific embryonic antigen-3 (SSEA-3), the sialic acid containing glycosphingolipids, the gangliosides GD2, GD3, GM2, fucosyl GM1, and Neu5GcGM3, and polysialic acid. Recent developments have furthered our understanding of the T-independent type II response that is seen in response to carbohydrate antigens. The selection of a vaccine target antigen is based on not only the presence of the antigen in a variety of tumor tissues but also on the role this antigen plays in tumor growth and metastasis. These roles for TACAs are being elucidated. Newly acquired knowledge in understanding the T-independent immune response and in understanding the key roles that carbohydrates play in metastasis are being applied in attempts to develop an effective vaccine response to TACAs. The role of each of the above mentioned carbohydrate antigens in cancer growth and metastasis and vaccine attempts using these antigens will be described., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Background and Objectives: Congenital toxoplasmosis is associated with significant morbidity and mortality. This study investigates the prevalence of toxoplasmosis among pregnant women., Design and Setting: A retrospective study at King Khalid University Hospital, Riyadh from September 2009 to August 2010., Patients and Methods: 0 Laboratory data of 2176 pregnant women screened for Toxoplasma gondii in the antenatal care unit were assessed during the study period. The mean (SD) age of the women and the duration of pregnancy were 25 (7.3) years and 18 (7.7) weeks, respectively. Data were extracted for the presence or absence of anti-T gondii immunoglobulin G (IgG) and IgM antibodies., Results: Of 2176 sera tested, 1351 (62%) did not show any evidence of exposure to T gondii. The remaining 825 (38%) samples tested positive for anti-T gondii IgG antibodies, and none was found to have anti-T gondii IgM antibodies in the serum. These data reveal that a significantly high number of women in the antenatal care unit at King Khalid University Hospital in Riyadh had been exposed to T gondii., Conclusion: A high prevalence of toxoplasmosis among pregnant women warrants multicenter community-based investigations for assessment of T gondii infection and identification of risk factors for transmission of toxoplasmosis in general, and particularly during pregnancy.