Levels and Trends of Maternal Mortality and Morbidity by Cause in North Africa and Middle East, 1990 to 2019: An Analysis for the Global Burden of Disease Study 2019

Background: Since 1990, the maternal mortality significantly decreased at global scale as well as the North Africa and Middle East. However, estimates for mortality and morbidity by cause and age at national scale in this region are not available. Method(s): This study is part of the Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Here we report maternal mortality and morbidity by age and cause across 21 countries in the region from 1990 to 2019. Result(s): Between 1990 and 2019, maternal mortality ratio (MMR) dropped from 148.8 (129.6-171.2) to 94.3 (73.4-121.1) per 100 000 live births in North Africa and Middle East. In 1990, MMR ranged from 6.0 (5.3-6.8) in Kuwait to 502.9 (375.2-655.3) per 100 000 live births in Afghanistan. Respective figures for 2019 were 5.1 (4.0-6.4) in Kuwait to 269.9 (195.8-368.6) in Afghanistan. Percentages of deaths under 25 years was 26.0% in 1990 and 23.8% in 2019. Maternal hemorrhage, indirect maternal deaths, and other maternal disorders rank 1st to 3rd in the entire region. Ultimately, there was an evident decrease in MMR along with increase in socio-demographic index from 1990 to 2019 in all countries in the region and an evident convergence across nations. Conclusion(s): MMR has significantly declined in the region since 1990 and only five countries (Afghanistan, Sudan, Yemen, Morocco, and Algeria) out of 21 nations didn't achieve the Sustainable Development Goal (SDG) target of 70 deaths per 100 000 live births in 2019. Despite the convergence in trends, there are still disparities across countries.Copyright © 2022 Academy of Medical Sciences of I.R. Iran. All rights reserved.

The effect of Metformin on improving survival among obese and diabetic patients affected with COVID-19: findings of a meta-analysis

Background: Diabetes Mellitus is one of the major non-communicable diseases among patients suffering from COVID-19, which increases the likelihood of hospital admission mortality. While Metformin has been found effective in reducing the mortality associated with COVID-19, there is a need to update the existing meta-analyses and quantitively synthesize the findings regarding the effect of Metformin in reducing mortality. Methods: We undertook a meta-analysis of 21 studies after searching for epidemiological studies systematically in PubMed/Medline, EMBASE, and Science Direct. We used odds ratios and their respective 95% confidence interval (CI) for a binary outcome, which was mortality, to examine the effect of Metformin on mortality. Heterogeneity was assessed using the I2 statistic and Q-test statistics. We evaluated the publication bias using a funnel plot, which was further confirmed by eager test statistics. A p-value of < 0.05 was considered statistically significant. Results: Overall, the findings revealed that Metformin reduced mortality by about 35%, and the results were statistically significant (OR= 0.66;95% CI 0.62 to 0.69;p < 0.05). This revealed that patients who took Metformin had improved survival by more than one-third than those who were not given Metformin. We found a relatively higher heterogeneity with an I2 value of 85.60% (Chi-squared = 138.85). The inverted funnel plot for the findings for the effect of Metformin on mortality was asymmetrical with test statistics for an eager test of -3.64 and a P-value of 0.002. Conclusion: The present updated meta-analysis revealed a positive effect of Metformin in reducing mortality among diabetic patients suffering from COVID-19. However, before implementing Metformin at a larger scale, clinicians and endocrinologists need to assess the risks versus benefits associated with Metformin for diabetic patients of COVID-19. Also, future studies are warranted to investigate the effects of Metformin for non-diabetic patients.

Infection-Control knowledge, attitude, practice and risk perception of occupational exposure to COVID-19 among dentists: A cross-sectional survey.

Background: COVID-19 is a potentially fatal disease that was announced as a global pandemic at the beginning of the year 2020. Aim: The purpose of our cross-sectional study was to evaluate the infection-control knowledge, attitude, practice, and risk perception of occupational exposure to COVID-19 among multinational dentists. Patients and Methods: A self-designed, 33-item, English questionnaire was created and distributed through social media and digital communication platforms. The questionnaire covered the demographic data, knowledge and perception of the occupational risk of the COVID-19 infections, and compared some infection control measures taken before and after this global pandemic. The results were analyzed, and four scores were used to assess the aforementioned parameters. Results: A total of 300 multinational dentists answered our survey, with the majority being females (59%) and aging from 25 to 44 years old (68%). We found that a statistically significant relationship exists between attitude and nationality, country of practice, medical condition, and the practicing specialty (P < 0.05). In addition, risk perception had a statistically significant correlation with nationality, smoking habits, education level, and specialty (P < 0.05). Furthermore, there was a statistically significant correlation between the practice score and the gender, age, smoking habits, education level, nature of the practice (private or governmental), and academia affiliation (P < 0.05). Conclusions: The study sample had good compliance with the instructions and guidelines of the World Health Organization (WHO) and the Centre for Disease Control (CDC), with most of them improving their infection control precautions after the virus's emergence according to the said guidelines. Furthermore, our participants were fearful of the COVID-19 virus and the fact of being potential transmitters. Despite saying that, the significant majority of them reported being confident in treating COVID-19-positive patients.

Skin problems related to personal protective equipment and personal hygiene measures during COVID-19 pandemic among healthcare workers in Aseer Region, Saudi Arabia

Background: Reports revealed rising levels of skin diseases secondary to protective equipment use. Healthcare providers who are working day and night during the pandemic of COVID-19 are more susceptible to the damage of the skin. There is scarce published data about the incidence of skin disorders secondary to protective equipment use during the COVID-19 pandemic and what factors are associated in Saudi Arabia. Aim: Assessing the potential skin damage as a result of personal protection equipment (PPE) and intensive hygiene measures for healthcare providers during COVID-19 pandemic in Aseer region. Methods: This study a cross-sectional questionnaire-based study done in Aseer region from January to October 2021. Personal data and related to history of skin disease, practices toward personal protective equipment, and new skin damage was collected and analyzed. Independent t-test and chi-square test was used to determine factors associated with the incidence of new skin damage during the COVID-19 pandemic. Results: Total of 214 participants was included in the study. (47.7%) of the participants reported experiencing new skin damage during the COVID-19 pandemic, while 112 (52.3%) of the participants did not. Age, having a history of chronic skin disease, and number of worn gloves layers were all significantly associated with the incidence of skin damage during COVID-19 pandemic. Conclusion: The considerable rate of new skin damage during the COVID-19 pandemic makes it essential to take action and start rising awareness toward this topic among health-care workers as well as teaching them how to prevent the incidence of new skin damage.

SARS-CoV-2-induced croup in paediatric patients in Aseer region, south-western, Saudi Arabia: Two case reports

Croup is a relatively mild and self-limiting disease but may occasionally be associated with severe morbidity in young children. While Para influenza is known to be the most common etiologic agent, other viruses can also cause croup that may be more severe. We report two cases (20 months, 3 years old) with classic symptoms and signs suggestive of croup, one case had severe croup that required admission to paediatric intensive care unit, the other one had moderate croup who admitted to the paediatric general ward in March and April 2021. Both were diagnosed with severe acute respiratory syndrome coronavirus (SARS-CoV-2) by polymerase chain reaction testing from nasopharyngeal samples that were negative for all other pathogens including the most common etiologies of croup. In conclusion, croup is one of the respiratory symptoms of novel SARS-CoV-2 in children therefore;the presence of clinical manifestations of croup indicates the need for COVID-19 screening.

Clinical characteristics and risk factors of patients with severe COVID-19 in Riyadh, Saudi Arabia: A retrospective study

Background: COVID-19 is newly emerging infectious disease that spread globally at unpredictable and unique pattern to the extent that the World Health Organization announced COVID-19 as a pandemic in the first couple months of 2020. This study aims to describe clinical and demographic features of COVID-19 patients and the influence of various risk factors on the severity of disease. Methods: This research is a retrospective study based on Saudi Arabia's ministry of health's Covid-19 data. The analysis relies on data of all COVID-19 patients recorded in Riyadh between 1st, March 2020 and 30th, July 2020. Statistical analyses were performed to investigate the effect of demographic characteristic, clinical presentation, and comorbidities on infection severity. Results: A total number of 1026 COVID-19 patients were identified based on the demographic data as follows: 709 cases (69% of cases) were males and 559 cases (54% of cases) were Saudi. Most of patients were diagnosed with mild signs and symptoms 697 (68% of cases), while 164 patient (16% of cases) demonstrated moderate signs and symptoms, and 103 cases (10%) were severe and 62 (6%) had critical febrile illness. Fever, cough, sore throat, and shortness of breath were the most common symptoms among patients with COVID-19. Among studied comorbidities in COVID-19 patients, diabetes mellitus and hypertension were the most prevalent. The results from the bivariate logistic regression analysis revealed that older age, diabetes mellitus, asthma, smoking, and fever are associated with severe or critically ill cases. Conclusion: The findings of this study show that old age, fever, and comorbidities involving diabetes mellitus, asthma, and smoking were significantly associated with infection severity. (c) 2021 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Real-time privacy preserving framework for Covid-19 contact tracing

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual's location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual's PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users. © 2021 Tech Science Press. All rights reserved.

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. © 2021 Yar Muhammad et al.

Leveraging Convolutional Neural Network for COVID-19 Disease Detection Using CT Scan Images

In 2020, the world faced an unprecedented pandemic outbreak of coronavirus 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 recommended 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 diagnose 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.

Mutual Authentication Scheme for the Device-to-Server Communication in the Internet of Medical Things

Internet of Medical Things (IoMT) is an application-specific extension of the generalized Internet of Things (IoT) to ensure reliable communication among devices Ci, designed for the medical industry. However, a challenging issue associated with these networks, i.e., IoMT and IoT, is to ensure the authenticity of both source and destination modules and further guarantee the integrity of the multimodal data the emergencies such as the COVID-19 pandemic. Various mechanisms for device authentication have been presented in the literature to resolve both devices and data’s authenticity, integrity, and privacy. Still, authentication of mobile device-to-server (in both homogeneous and heterogeneous IoMT) is not explicitly addressed for the black-hole attack. In this paper, a device-to-server and vice versa mutual authentication scheme are presented to ensure secure communication sessions among numerous mobile devices Ci and server Sj in the operational IoMT. The proposed scheme is a hybrid of Medium Access Control (MAC) and enhanced on-demand vector (EAODV)-enabled routing schemes. In the proposed scheme, an offline phase is introduced to complete the registration process of member devices with the concerned server module. It blocks every possible entry of the potential intruder devices Ak in the operational IoMT. A mobile device, Ci, interested in initiating a communication session with a particular Server Sj, is needed to pass the mutual authentication process. As a result, only registered devices Ci are allowed to communicate. Additionally, a reliable encryption and decryption scheme is used to ensure data reliability during these communication sessions. Simulation results verify the exceptional performance of the proposed mutual authentication scheme in terms of authenticity, security, and integrity of both devices and data in the operational IoMT. IEEE