Estimate of immunoglobulin G, M, and some liver enzymes levels in Covid-19 patients and relation with blood group in Najaf governorate, Iraq

The COVID19 disease is a transmittable viral infection that causes acute respiratory system infection, to this day there is no proven treatment for this virus and its complication in the body are still unclear. so the current study aimed to determine the levels of immunoglobulin (M, G) against infection with covid 19, the measure of liver enzymes(AST, ALT), and the relation of infection with blood group. This study included 60 patients infected by COVID-19 and 30 uninfected people, who came to the AL-Najaf Hospitals from January to March month 2020. Draw 5 ml of blood for the measure of G, M, and AST, ALT levels, and determine the blood group. The results showed that infection with the Covid-19 virus had a significant effect (p <0.001) on the level of both G and M antibodies compared to the control group (10.18, 16.94) mg/dl, (0.320,0.312) mg/dl, respectively. also, the study showed the significant effect of infection on liver enzymes which caused increased AST and ALT levels (44.25,52.30)U/L compared with the control group (36.28, 42.46) U/L respectively.also explained the relation between blood group and covid 19 infection, as a blood group A recorded the highest rate of infection and blood type O lowest rate of infection (35, 13.33) % respectively. so it is possible to rely on measuring the level of G, M antibodies in diagnosing or recovering from covid 19 infection.also, know the effect of the infection on the liver and the relationship between infection and blood group. © 2023 Author(s).

Hayyakum (حيَّاكم) - COVID-19 Vaccine Digital Certificate: A Blockchain Approach

As a consequence of the global pandemic, many restrictions and rules were enforced. One predicament was the travel restrictions and requirements put into place with regard to vaccinations. Countries worldwide now require people to be vaccinated upon entry. The process of validating vaccine doses requires lots of paperwork and is inefficient. Blockchain is an uprising technology that is secure and fast at carrying out transactions. We propose implementing vaccine dose verifications between countries through vaccine certificates using Blockchain as an effective solution. The need for a common shared database, avoiding a trusted third party to administrate the network, having several countries involved, ensuring privacy and security, and accountability logs make Blockchain needed in this scenario. Digital vaccine certificates are very sensitive information that must be kept private and secure but accessible to several entities. Blockchain ensures the aforementioned requirements are met while preserving the integrity of the VDCs. This paper describes blockchain technology and its application in digital vaccine certificates. © 2022 IEEE.

A Hybrid AI Model for Improving COVID-19 Sentiment Analysis in Social Networks

The recent COVID-19 (novel coronavirus disease) pandemic induced a deep polarization among regional as well as global communities. The sentiments regarding the pandemic and its impact on lifestyle and economy, often expressed via social networks, are regarded as critical metrics for capturing such polarization and formulating appropriate intervention by the relevant authorities. While there exist a myriad of Natural Language Processing (NLP) models for mining social media data, we demonstrate the shortcomings of the individual models in this paper, and explore how to improve the COVID-19 sentiment analysis in social media network data via two hybrid predictive models based on a Long-Short-Term-Memory (LSTM)-based autoencoder and a Convolutional Neural Network (CNN) model coupled with a bi-directional LSTM. Through extensive experiments on the recently acquired Twitter dataset, we compare the COVID-19 sentiments exhibited in the USA and Canada using our proposed hybrid predictive models and demonstrate their superiority over individual Artificial Intelligence (AI) models. © 2022 IEEE.

Social Interaction in Virtual Shopping

With the increasing popularity of e-commerce and the coronavirus situation, an increasing number of shoppers are opting for online shopping because of store closures and their fear of contracting the coronavirus in public. While conventional retail provides consumers with a full spectrum of interaction, online shopping has been deficient in these types of experiences. Therefore, the virtual reality technology is used to bridge the gap between the two shopping techniques and create a more natural and intuitive shopping environment. The aim of this research is to investigate how, in a virtual store environment, consumers' interaction affect their shopping experience. A virtual supermarket with the interaction facility was designed. An experiment was conducted to track the effect of the social interaction on the consumers using different metrics. The results showed that consumers' social interaction in the form of avatar-mediated communication has a beneficial impact on their social presence. It also demonstrates that consumers felt more immersed and socially engaged to the shopping environment via the social interaction among avatars.

Relationship between serum levels of procalcitonin, vitamin B12, vitamin D in patients with COVID-19

COVID-19 is a new infectious disease, for which there is currently no treatment. It is therefore necessary to explore biomarkers to determine the extent of lung lesions and disease severity. Objective. The study aimed to assess the usefulness of procalcitonin levels in the COVID-19 and to correlate them with other biomarkers. Methods. the collected the data, prospectively, all COVID-19 cases admitted in lab private (30) cases with COVID-19 pneumonia and (30) control patients had Reverse Transcription Polymerase Chain Reaction (RT-PCR) positive. laboratory analysis of inflammatory indices and organ function was accomplished for the sum total of cases and controls measured procalcitonin, CRP, B12, LDH. Result. Procalcitonin,CRP, LDH, D-dimer levels in patients groups the higher than those in the controls highly significant. Conclusion. In the early stage of COVID-19 procalcitonin levels were positively correlated with CRP, D-dimer, ferritin, LDH and negative correlated with B12.

COVID-19 outbreak investigation in a hospital using computational flow analysis

Introduction: Over the last two years, the COVID-19 has caused unprecedented disruption worldwide. Healthcare workers (HCW), particular those working in hospitals have been the most affected from increased risk of contracting COVID-19 from hospital environment and patient care. Although various efforts have been taken by the hospital to reduce the risk, however, outbreaks still continue to occur. This case study reports on an outbreak investigation using computation flow analysis to investigate an outbreak in a non-COVID-19 ward. Material and Methods: This is a case report of an outbreak that occurs in a non-COVID-19 ward in a teaching hospital in Malaysia. The outbreak investigation was conducted, which includes contact tracing, risk assessment, walk-through survey, airflow measurements and computational flow analysis (CFA). Results: The outbreak occurred in one of the five bedded cubicles in a non-COVID-19 ward. The index case was a patient that was admitted for non-COVID-19 related medical conditions. The index case subsequently transmitted the disease to three patients and one HCW. On initial assessment, the HCW was not considered to have acquired COVID-19 from the index case, as the HCW have no unprotected contact with the index case. However, after the walk-through survey assessment, it was noted that airflow may be a contributing factor. An airflow measurement and CFA was conducted and reviewed the possibility route of transmission. Conclusion: The use of airflow assessment and CFA should be considered in a respiratory diseases outbreak investigation.

A Deep Learning-based System for Detecting COVID-19 Patients

COVID-19 (Coronavirus) is a very contagious infection that has drawn the world public's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system's design is a challenging problem. In this paper, motivated by the outstanding performance of deep learning (DL) in many solutions, we used DL based approach for computer-aided design (CAD) of the COVID-19 detection system. For this purpose, we used a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness by using two benchmark publicly available datasets (Covid-Chestxray-Dataset and Chex-Pert Dataset). The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a 10-fold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2% and an Fl-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and Fl-score of our proposed system are high, and it performs better than the existing state-of-the-art systems. The proposed system based on DL will be helpful in medical diagnosis research and health care systems.

On COVID-19 Prediction Using Asynchronous Federated Learning-Based Agile Radiograph Screening Booths

To combat the novel coronavirus (COVID-19) spread, the adoption of technologies including the Internet of Things (IoT) and deep learning is on the rise. However, the seamless integration of IoT devices and deep learning models for radiograph detection to identify the presence of glass opacities and other features in the lung is yet to be envisioned. Moreover, the privacy issue of the collected radiograph data and other health data of the patients has also arisen much concern. To address these challenges, in this paper, we envision a federated learning model for COVID-19 prediction from radiograph images acquired by an X-ray device within a mobile and deployable screening resource booth node (RBN). Our envisioned model permits the privacy-preservation of the acquired radiograph by performing localized learning. We further customize the proposed federated learning model by asynchronously updating the shallow and deep model parameters so that precious communication bandwidth can be spared. Based on a real dataset, the effectiveness of our envisioned approach is demonstrated and compared with baseline methods.

UV-CDS: An Energy-Efficient Scheduling of UAVs for Premises Sterilization

Despite the severity of the second wave of the novel coronavirus disease (COVID-19) and the recent hope for vaccine roll-outs, many public and private institutions are forced to resume their activities subject to ensuring an adequate sterilization of their premises. The existing off-the-shelf drones for such environment sanitization have limited flight-time and payload-carrying capacity. In this paper, we address this challenge by formulating an optimization problem to minimize the energy consumed by drones equipped with ultraviolet-C band (UV-C) panels. To solve this computationally hard problem, we propose several heuristics, such as a randomized path selection algorithm whose solution is further improved with a genetic algorithm-based UV-C drone-based sterilization (UV-CDS) scheduling technique. We consider educational institutions, confronting increasing infections, as an important use-case for the problem. Due to the energy constraint of the drones, the number of drones required for sterilization of the campus is smartly altered for various campus scenarios. The respective energy-efficient paths in the proposed heuristics and our envisioned UV-CDS are estimated for the drones. The performance is evaluated through extensive computer-based simulations which clearly demonstrates the effectiveness of UV-CDS in terms of sub-optimal performance and much faster execution time in contrast with the other methods. IEEE

A Survey of Alcohol-Based Hand Sanitizers in Nairobi: Packaging, Labelling and Regulatory Compliance

Abstract: Alcohol based hand sanitizers are currently recommended for routine use in curbing the spread of the COVID-19 global pandemic. The present survey examined hand sanitizers marketed in Nairobi County with regards to product appearance, packaging, labelling and declared composition. Seventy-six samples were collected from five sites within the Nairobi metropolis - Central Business District, Kibera, Kilimani/Karen, Ngong and Thika. A wide range of non-conformities were observed for the criteria applied. Many samples had incomplete or missing label information, ingredient lists, cautionary warnings, Kenya Bureau of Standards (KEBS) standardization marks and permit numbers. Glycerin, fragrances and carbomers were the most common added ingredients. Poor formulation indicators such as haziness and phase separation were encountered in some products. The median price of the products was KES 250 (USD 2.36) per 100 ml although there was considerable variation in pricing of samples. None of the samples evaluated fully met all the standards for the parameters evaluated. Strict adherence to regulatory standards by producers of hand sanitizers is required to ensure that only compliant products are available on the market

Alcohol-Based Hand Sanitizers – A Multidimensional Perspective (preprint)

The global use of alcohol based hand sanitizers (ABHS) as a means of controlling the transmission of infectious disease increased dramatically in 2020 as governments and public health agencies across the world advocated hand hygiene as a preventative measure during the COVID-19 pandemic. Although the performance of these products is most commonly defined as a function of their alcohol concentration, they are multifaceted products in which an interplay of several factors is important in determining efficacy. The hand sanitizer tetrahedron, is a novel concept that considers both ABHS formulation factors and product performance factors from a multi-dimensional perspective. The four faces of the tetrahedron represent input/formulation factors: 1) the type and amount of alcohol, 2) inactive ingredients, 3) the type of formulation/delivery system and 4) manufacturing practices. The four corners of the tetrahedron represent output/product performance factors: 1) efficacy, 2) sensory characteristics, 3) usage, usability and compliance and 4) product safety/adverse effects. All factors are of importance to ensuring the effectiveness and utility of these products.