Distribution of influenza A and B antibodies and correlation with ABO/Rh blood grouping

Background: Influenza is a clinically-significant infection with significant number of globally reported annual deaths. The aim of this study was to study the distribution of influenza A and B antibodies in Najran, the Southwest region of Saudi Arabia, and to investigate the correlation between demographic characteristics and influenza virus antibody levels.Methods: Enzyme linked immunosorbent assay was used to detect antibody level of influenza A and B. The correlation with ABO/Rh blood groupings was also examined. The total number of participants was 252. Only twenty-four subjects received the flu vaccine.Results: It was found that 33.7% and 24.1% of unvaccinated subjects were IgG-positive for influenza A and B, respectively. Interestingly, the antibody levels of the unvaccinated participants were higher than the vaccinated group. A significant difference was found between unvaccinated participants with O+ and influenza A and B antibody levels (**p=0.0045). The antibody level was inversely correlated with age in influenza B IgG subjects but not influenza A IgG (r=-0.1379; R squared=0.01900; p=0.0375). Forty-three subjects (17%) were positive for antibodies of both influenza A and B.Conclusions: IgG antibody positivity is greater in cases of influenza type A compared to influenza B. A significant correlation was found in the unvaccinated group between influenza B IgG antibody levels and age, but not influenza A (*p=0.0375). More research is needed to investigate the role of O+ blood group in influenza infections

Contemporary Concepts of Neutrosophic Fuzzy Soft BCK-submodules

In this paper, we introduce the concept of neutrosophic fuzzy soft translations and neutrosophic fuzzy soft extensions of neutrosophic fuzzy soft BCK-submodules and discusse the relation between them. Also, we dene the notion of neutrosophic fuzzy soft multiplications of neutrosophic fuzzy soft BCK-submodules. Finally, we investigate some resultes

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing.

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV's residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal

Leveraging convolutional neural network for COVID-19 disease detection using CT scan images

In 2020, the world faced an unprecedented pandemic outbreak of cor-onavirus disease (COVID-19), which causes severe threats to patients suffering from diabetes, kidney problems, and heart problems. A rapid testing mechanism is a primary obstacle to controlling the spread of COVID-19. Current tests focus on the reverse transcription-polymerase chain reaction (RT-PCR). The PCR test takes around 4–6 h to identify COVID-19 patients. Various research has recom-mended AI-based models leveraging machine learning, deep learning, and neural networks to classify COVID-19 and non-COVID patients from chest X-ray and computerized tomography (CT) scan images. However, no model can be claimed as a standard since models use different datasets. Convolutional neural network (CNN)-based deep learning models are widely used for image analysis to diag-nose and classify various diseases. In this research, we develop a CNN-based diagnostic model to detect COVID-19 patients by analyzing the features in CT scan images. This research considered a publicly available CT scan dataset and fed it into the proposed CNN model to classify COVID-19 infected patients. The model achieved 99.76%, 96.10%, and 96% accuracy in training, validation, and test phases, respectively. It achieved scores of 0.986 in area under curve (AUC) and 0.99 in the precision-recall curve (PRC). We compared the model’s performance to that of three state-of-the-art pretrained models (MobileNetV2, InceptionV3, and Xception). The results show that the model can be used as a diagnostic tool for digital healthcare, particularly in COVID-19 chest CT image classification

Applications of Soft Sets in -Algebras

In 1999, Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty and vagueness. In this paper, we apply the concept of soft sets to K-algebras and investigate some properties of Abelian soft K-algebras. We also introduce the concept of soft intersection K-algebras and investigate some of their properties

Bimetallic metal-organic frameworks for controlled catalytic graphitization of nanoporous carbons

Single metal-organic frameworks (MOFs), constructed from the coordination between one-fold metal ions and organic linkers, show limited functionalities when used as precursors for nanoporous carbon materials. Herein, we propose to merge the advantages of zinc and cobalt metals ions into one single MOF crystal (i.e., bimetallic MOFs). The organic linkers that coordinate with cobalt ions tend to yield graphitic carbons after carbonization, unlike those bridging with zinc ions, due to the controlled catalytic graphitization by the cobalt nanoparticles. In this work, we demonstrate a feasible method to achieve nanoporous carbon materials with tailored properties, including specific surface area, pore size distribution, degree of graphitization, and content of heteroatoms. The bimetallic-MOF-derived nanoporous carbon are systematically characterized, highlighting the importance of precisely controlling the properties of the carbon materials. This can be done by finely tuning the components in the bimetallic MOF precursors, and thus designing optimal carbon materials for specific applications

Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease