A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing.

Coronavirus (COVID-19) is a very contagious infection that has drawn the world'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 design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use 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 using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. 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 tenfold 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 F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.

Development and validation of a predictive scoring system for in-hospital mortality in COVID-19 Egyptian patients: a retrospective study.

SARS-CoV-2 virus has rapidly spread worldwide since December 2019, causing COVID-19 disease. In-hospital mortality is a common indicator for evaluating treatment outcomes. Therefore, the developing and validating a simple score system from observational data could assist in modulating the management procedures. A retrospective cohort study included all data records of patients with positive PCR for SARS-CoV-2. The factors that associated with mortality were analyzed, then allocation of potential predictors of mortality was executed using different logistic regression modeling, subsequently scoring system was developed from the most weighted predictors. The mortality rate of patients with COVID-19 pneumonia was 28.5% and 28.74%, respectively. The most significant factors that affected in-hospital mortality were old age (> 60 years), delay in hospital admission (> 4 days), high neutrophil/lymphocyte ratio "NLR" (> 3); higher computed tomography severity score; and CT-SS (> 20), in addition to using remdesivir and tocilizumab in the treatment protocol (P < 0.001 for all). The validity of the newly performed score was significant; the AUC was 85%, P < 0.001, and its prognostic utility was good; the AUC was 75%, P < 0.001. The prognostic utility of newly developed score system (EGY.Score) was excellent and could be used to adjust the treatment strategy of highly at-risk patients with COVID-19 pneumonia.

Sinopharm's BBIBP-CorV Vaccine and ChAdOx1 nCoV-19 Vaccine Are Associated with a Comparable Immune Response against SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide. During the early stages of vaccination in Egypt, the ChAdOx1 nCoV-19 and BBIBP-CorV vaccines were the most distributed. The aim of this study was to compare the immune responses and short-term efficacies of these two vaccines. We recruited adults who received two doses of either vaccine. Samples were collected after the first dose of ChAdOx1 nCoV-1 and after the second dose of both vaccines. Antibodies against SARS-CoV-2 antigens were measured using LABScreen™ COVID Plus kits, and cell-mediated immune responses were assessed using flow cytometry. Of the 109 recruited subjects, 60 (55%) received the ChAdOx1 nCoV-19 vaccine, and the remainder received the BBIBP-CorV vaccine. The total antibody level did not significantly differ between the two groups. The level of the anti-spike subunit 2 (S2) antibody was significantly higher in the ChAdOx1 nCoV-19 group. The percentages of both total T cells and B cells were unaffected by the type of vaccination. However, the ChAdOx1 nCoV-1 vaccine was significantly associated with a higher percentage of CD8+ cells. The vaccines did not significantly differ in the number or severity of infections postvaccination. None of the participants were admitted to the hospital or died of COVID-19 infection. In conclusion, the BBIBP-CorV vaccine is associated with an immune response and protection against infection that is comparable to that of the ChAdOx1 nCoV-1 vaccine. Follow-up is needed to study the long-term protective effects of both vaccines. Inactivated vaccines are easier to manufacture in developing countries and their limited side effects may lead to better economic benefits by limiting the number of absences from work.

Predictors of post-COVID symptoms in Egyptian patients: Drugs used in COVID-19 treatment are incriminated.

OBJECTIVES: COVID-19 is a multisystem disease, and some patients suffer from physical or psychological symptoms for weeks or even months after infection, which is described as post-COVID syndrome. The goal of this study is evaluating the prevalence of post-COVID-19 symptoms among Egyptian patients and detecting the factors associated with the presence of these symptoms. METHODS: An on-line cross-sectional survey using Google Forms was used to conduct the present study, which took place between June and August 2021. RESULTS: Three hundred and ninety-six participants filled in the survey. The mean age of participants was 41.4 years. Most participants had mild to moderate COVID-19 (81.31%). The prevalence of post-COVID-19 symptoms was 87.63%, where the most frequent symptom was fatigue (60.86%). Female sex, the presence of comorbidities, lower degree of education, longer disease duration, as well as severe and critical forms of the disease were significantly associated with the presence of post-COVID symptoms. Using regression analysis, the predictors of post-COVID symptoms were severe and critical forms of the disease and intake of antibiotics and corticosteroids for treatment of COVID-19. CONCLUSIONS: COVID-19 is followed by high prevalence of post-COVID symptoms. To the best of our knowledge, this is the first study to report the relationship between the use of antibiotics and the development of post-COVID symptoms. We recommend further studies to understand this relationship. We also recommend restricting the use of these drugs to indicated cases according to the international guidelines. More studies are needed to gain better understanding of post-COVID symptoms especially in females.

HLA-B*15 predicts survival in Egyptian patients with COVID-19.

Genetic differences among individuals could affect the clinical presentations and outcomes of COVID-19. Human Leukocyte Antigens are associated with COVID-19 susceptibility, severity, and prognosis. This study aimed to identify HLA-B and -C genotypes among 69 Egyptian patients with COVID-19 and correlate them with disease outcomes and other clinical and laboratory data. HLA-B and -C typing was performed using Luminex-based HLA typing kits. Forty patients (58%) had severe COVID-19; 55% of these patients died, without reported mortality in the moderate group. The alleles associated with severe COVID-19 were HLA-B*41, -B*42, -C*16, and -C*17, whereas HLA-B*15, -C*7, and -C*12 were significantly associated with protection against mortality. Regression analysis showed that HLA-B*15 was the only allele associated with predicted protection against mortality, where the likelihood of survival increased with HLA-B*15 (P < 0.001). Patient survival was less likely to occur with higher total leukocytic count, ferritin, and creatinine levels. This study provides interesting insights into the association between HLA class I alleles and protection from or severity of COVID-19 through immune response modulation. This is the first study to investigate this relationship in Egyptian patients. More studies are needed to understand how HLA class I alleles interact and affect Cytotoxic T lymphocytes and natural killer cell function.

A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept.

The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial-terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach.

A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept

The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial-terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. Graphical

Hepatic steatosis: a risk factor for increased COVID-19 prevalence and severity-a computed tomography study.

BACKGROUND: Around 25% of the world population was affected by the metabolic-related fatty liver disorder. Hepatic steatosis is frequently observed in conjunction with hypertension, obesity comorbidities, and diabetes. We evaluate the hepatic steatosis frequency found in chest CT exams of COVID-19-positive cases compared to non-infected controls and evaluate the related increased prevalence and severity of COVID. RESULTS: Our research includes 355 subjects, 158 with positive PCR for COVID-19 (case group) and 197 with negative PCR and negative CT chest (control group). The mean age in the positive group was 50.6 ± 16 years, and in the control, it was 41.3 ± 16 years (p < 0.001). Our study consists of 321 men (90.5%) and 34 women (9.5%). The number of males in both cases and control groups was greater. In the case group, 93% men vs. 6.9% women, while in controls, 88.3% men vs.11.6% women, p < 0.001. CT revealed normal results in 55.5% of individuals (i.e., CORADs 1) and abnormal findings in 45.5% of participants (i.e., CORADs 2-5). In abnormal scan, CO-RADs 2 was 13.92%, while CO-RADs 3-4 were 20.89% of cases. CO-RADs 5 comprised 65.19% of all cases. Approximately 42.6% of cases had severe disease (CT score ≥ 20), all of them were CO-RADs 5. The PCR-positive class had a greater prevalence of hepatic steatosis than controls (28.5% vs.12.2%, p < 0.001). CO-RADs 2 represented 11.1%, CO-RADs 3-4 represented 15.6%, and CO-RADs 5 represented 73.3% in the hepatic steatosis cases. The mean hepatic attenuation value in the case group was 46.79 ± 12.68 and in the control group 53.34 ± 10.28 (p < 0.001). When comparing patients with a higher severity score (CT score ≥ 20) to those with non-severe pneumonia, it was discovered that hepatic steatosis is more prevalent (73.2% vs. 26.8%). CONCLUSIONS: Steatosis was shown to be substantially more prevalent in COVID-19-positive individuals. There is a relation among metabolic syndrome, steatosis of the liver, and obesity, as well as the COVID-19 severity.

Upregulation of FOXP3 is associated with severity of hypoxia and poor outcomes in COVID-19 patients.

The levels of messenger RNA (mRNA) transcription of FOXP3, IFN-γ, TNF, IL-6 and COX-2 from both COVID-19 infected and control subjects were evaluated using SYBRTM green real-time polymerase chain reaction (RT-PCR). Severe/critical cases showed significantly lower lymphocyte counts and higher neutrophil counts than the mild or moderate cases. There were significantly lower levels of mRNA expressions of IFN-γ, TNFα and FOXP3 in COVID-19 patients than in the control group. On the other hand, IL-6 and COX-2 expressions were significantly higher in patients suffering from severe disease. FOXP3 expressions were correlated with the severities of hypoxia and were excellent in predicting the disease severity. This was followed by the IL-6, COX-2 and TNFα expressions. FOXP3 expression was the only biomarker to show a significant correlation with patient mortality. It was concluded that SARS-CoV-2 infection is associated with the downregulation of FOXP3 and upregulations of IL-6 and COX-2.

Virtual educational meetings and activities during the COVID-19 pandemic and beyond: Egyptian oncologists' experience.

The COVID-19 pandemic has had ramifications for most healthcare activities, including medical education and communication aspects. Virtual educational meetings and activities (VEMAs) have been utilised tremendously in the pandemic era, reflecting a transition to new horizons of cyberspace. This creates the need to explore possible challenges for the implementation of such services in the rapidly evolving field of oncology. The aim of our study is to explore the impact of the COVID-19 pandemic on VEMAs in the oncology community in Egypt. It focused on the evaluation of current attitudes, satisfaction and expectations of Egyptian oncologists during and beyond the COVID-19 era. The study is a cross-sectional study using a survey that was distributed through social media. It targeted Egyptian oncologists during the months of May and June 2020. A total of 118 participants completed the survey and most of them were younger than 35 years (71%). Most participants (93.2%) agreed that COVID-19 affected the stream of live medical educational meetings. About three-quarters of them attended VEMAs during the COVID-19 period compared to 50% prior to the pandemic. The majority reported that evening hours after 8 PM was the best time to attend VEMAs and 1 hour is the optimal duration for a virtual meeting. Although the COVID-19 pandemic appeared as an unprecedented challenge for medical education, it can be a catalyst for VEMAs, especially in a rapidly evolving field such as oncology. Further research is needed to assess whether learners are ready and willing to make greater use of online educational platforms and investigate the possible barriers and strategies to enhance their use.

n-Gram based language processing using Twitter dataset to identify COVID-19 patients.

Due to the rapid growth of electronic documents, e.g., tweets, blogs, Facebook posts, snaps in different languages that use the same writing script, language categorization, and processing have great importance. For instance, to identify COVID-19 positive patients or people's emotions on COVID-19 pandemic from tweets written in 35 different languages faster and accurate, language categorization and processing of tweets is significantly essential. Among many language categorization and processing techniques, character and word n-gram based techniques are very popular and simple but very efficient for categorizing and processing both short and large documents. One of the fundamental problems of language processing is the efficient use of memory space in implementing a technique so that a vast collection of documents can be easily categorized and processed. In this paper, we introduce a framework that categorizes the language of tweets using n-gram based language categorization technique and further processes the tweets using the machine-learning approach, Linear Support Vector Machine (LSVM), that may be able to identify COVID-19 positive patients. We evaluate and compare the performance of the proposed framework in terms of language categorization accuracy, precession, recall, and F-measure over n-gram length. The proposed framework is scalable as many other applications that involve extracting features and classifying languages collected from social media, and different types of networks may use this framework. This proposed framework, also being a part of health monitoring and improvement, tends to achieve the goal of having a sustainable society.

Prevalence, Associated Factors, and Consequences of Burnout Among Egyptian Physicians During COVID-19 Pandemic.

Objectives: COVID-19 has been recognized as a pandemic by the World Health Organization, and physicians are at the frontline to confront the disease. Burnout syndrome (BOS) is a syndrome resulting from chronic workplace stress that has not been successfully managed. The objective of this study is to evaluate the frequency and associated risk factors of BOS among a sample of Egyptian physicians during the COVID-19 pandemic. Methods: Using Maslach Burnout Inventory Human Services Survey, a cross-sectional electronic survey was conducted to assess BOS among the target group. Results: Two hundred and twenty physicians participated in the study. The frequency of BOS among the research group was 36.36%. The possibility of development of BOS increased two times with the need to buy personal protective equipment (PPE) from participants' own money, with harassment by patients' families, and was less likely to develop in doctors with older age. While male gender was a predictor of depersonalization (DP), female gender showed a significant association with higher emotional exhaustion (EE). Infection or death from COVID-19 among colleagues or relatives showed significant association with elevated EE and lowered personal achievement (PA), respectively. Conclusion: COVID-19 pandemic added new factors to the development of BOS in our research group. Several measures should be taken to support physicians at this stage. These measures include psychological support, organizing work hours, adjusting salaries, and providing personal protective equipment and training on safety measures.