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Background: The ongoing global crisis of Higher Education (HE) institutions during the post-COVID-19 pandemic period has increased the likelihood of enduring psychological stressors for staff. This study aimed to identify factors associated with job insecurity, burnout, psychological distress and coping amongst staff working at HE institutions globally., Methods: An anonymous cross-sectional study was conducted in 2023 with staff at HE institutions across 16 countries. Job insecurity was measured using the Job Insecurity Scale (JIS), burnout using the Perceived Burnout measure question, psychological distress using the Kessler Psychological Distress Scale (K10), and coping using the Brief Resilient Coping Scale. Multivariable logistic regression with a stepwise variable selection method was used to identify associations., Results: A total of 2,353 staff participated; the mean age (± SD) was 43(± 10) years and 61% were females. Most staff (85%) did not feel job insecurity, one-third (29%) perceived burnout in their jobs, more than two-thirds (73%) experienced moderate to very high levels of psychological distress, and more than half (58%) exhibited medium to high resilient coping. Perceived job insecurity was associated with staff working part-time [Adjusted Odds Ratio 1.53 (95% Confidence Intervals 1.15-2.02)], having an academic appointment [2.45 (1.78-3.27)], having multiple co-morbidities [1.86 (1.41-2.48)], perceived burnout [1.99 (1.54-2.56)] and moderate to very high level of psychological distress [1.68 (1.18-2.39)]. Perceived burnout was associated with being female [1.35 (1.12-1.63)], having multiple co-morbidities [1.53 (1.20-1.97)], perceived job insecurity [1.99 (1.55-2.57)], and moderate to very high levels of psychological distress [3.23 (2.42-4.30)]. Staff with multiple co-morbidities [1.46 (1.11-1.92)], mental health issues [2.73 (1.79-4.15)], perceived job insecurity [1.61 (1.13-2.30)], and perceived burnout [3.22 (2.41-4.31)] were associated with moderate to very high levels of psychological distress. Staff who perceived their mental health as good to excellent [3.36 (2.69-4.19)] were more likely to have medium to high resilient coping., Conclusions: Factors identified in this study should be considered in reviewing and updating current support strategies for staff at HE institutions across all countries to reduce stress and burnout and improve wellbeing., (© 2024. The Author(s).)

Background/objectives: In people with type 2 diabetes mellitus (T2DM), low carbohydrate diets (LCD), defined as 10-<26% total energy intake from carbohydrate, have indicated improved glycaemic control and clinical outcomes. Web-based interventions can help overcome significant challenges of accessibility and availability of dietary education and support for T2DM. No previous study had evaluated a web-based LCD intervention using a randomised controlled trial (RCT) design. The objective of this study was to assess whether a web-based LCD programme provided in conjunction with standard care improves glycaemic control in adults with T2DM., Subjects/methods: A 16-week parallel RCT was conducted remotely during Covid-19 among the general community, recruiting adults with T2DM not on insulin aged 40-89 years. Participants were randomly assigned (1:1) to standard care plus the web-based T2Diet healthy LCD education programme (intervention) or standard care only (control). The primary outcome was haemoglobin A1c (HbA1c). Secondary outcomes were weight, body mass index (BMI), anti-glycaemic medication, dietary intake, and self-efficacy. Blinded data analysis was conducted by intention-to-treat., Results: Ninety-eight participants were enrolled, assigning 49 to each group, with 87 participants (n = 40 intervention; n = 47 control) included in outcome analysis. At 16 weeks, there was a statistically significant between-group difference favouring the intervention group, with reductions in HbA1c -0.65% (95% CI: -0.99 to -0.30; p < 0.0001), weight -3.26 kg (p < 0.0001), BMI -1.11 kg/m 2 (p < 0.0001), and anti-glycaemic medication requirements -0.40 (p < 0.0001), with large effect sizes Cohen's d > 0.8., Conclusion: This study demonstrated that as an adjunct to standard care, the web-based T2Diet programme significantly improved glycaemic control and clinical outcomes in adults with T2DM. In addition, the results highlight the potential to improve access and availability for people with T2DM to achieve glycaemic control and improved health through web-based dietary education and support., (© 2023. Springer Nature Limited.)

COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.

COVID-19 was accompanied with the increasing broadcast of fake news, misinformation and excessive information via social media platforms. This phenomenon has been termed "infodemic", to describe an overwhelming amount of mostly fake, false or inaccurate information which spreads rapidly and impacts negatively on achieving a solution. It would therefore be desirable to use a cautious approach which utilizes culturally sensitive and country specific measures to deal with this occurrence. We aim to raise awareness, likewise draw the attention of global scientific community on this topic of public and mental health concern and it calls for further comments on this issue.

This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, specificity, AUC, accuracy, etc.) make selecting the best performance model difficult. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models. We see that when the proposed method is used for analysis using the Extra-Trees classifier, it has achieved promising results (AUC: 0.95, Precision: 1, Recall: 0.97)., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Disease screening identifies a disease in an individual/community early to effectively prevent or treat the condition. COVID-19 has restricted hospital visits for screening and other healthcare services resulting in the disruption of screening for cancer, diabetes, and cardiovascular diseases. Smartphone technologies, coupled with built-in sensors and wireless technologies, enable the smartphone to function as a disease-screening and monitoring device with negligible additional costs and potentially higher quality results. Thus, we sought to evaluate the use of smartphone applications for disease screening and the acceptability of this technology in the medical and healthcare sectors. We followed a systematic review process using four databases, including Medline Complete, Web of Science, Embase, and Proquest. We included articles published in English examining smartphone application utilisation in disease screening. Further, we presented and discussed the primary outcomes of the research articles and their statistically significant value. The initial search yielded 1046 studies for the initial title and abstract screening. Of the 105 articles eligible for full-text screening, we selected nine studies and discussed them in detail under four main categories: an overview of the literature reviewed, participant characteristics, disease screening, and technology acceptance. According to our objective, we further evaluated the disease-screening approaches and classified them as clinically administered screening (33%, n = 3), health-worker-administered screening (33%, n = 3), and home-based screening (33%, n = 3). Finally, we analysed the technology acceptance among the users and healthcare practitioners. We observed a significant statistical relationship between smartphone applications and standard clinical screening. We also reviewed user acceptance of these smartphone applications. Hence, we set out critical considerations to provide equitable healthcare solutions without barriers when designing, developing, and deploying smartphone solutions. The findings may increase research opportunities for the evaluation of smartphone solutions as valid and reliable screening solutions.

Introduction: To reduce mortality in hospitalized patients with COVID-19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID-19., Methods: This study was conducted on 660 hospitalized patients with CVD and COVID-19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID-19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation., Results: Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID-19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation., Conclusions: Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for COVID-19., (© 2021 The Authors. Immunity, Inflammation and Disease published by John Wiley & Sons Ltd.)

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems., (© 2022. The Author(s).)

Competing Interests: Competing interests: The authors completed the ICMJE Declaration of Interest Form (available upon request from the corresponding author), and declare no conflicts of interest.

Competing Interests: Competing interests: None declared.

Background: The current pandemic of COVID-19 impacted the psychological wellbeing of populations globally., Objectives: We aimed to examine the extent and identify factors associated with psychological distress, fear of COVID-19 and coping., Methods: We conducted a cross-sectional study across 17 countries during Jun-2020 to Jan-2021. Levels of psychological distress (Kessler Psychological Distress Scale), fear of COVID-19 (Fear of COVID-19 Scale), and coping (Brief Resilient Coping Scale) were assessed., Results: A total of 8,559 people participated; mean age (±SD) was 33(±13) years, 64% were females and 40% self-identified as frontline workers. More than two-thirds (69%) experienced moderate-to-very high levels of psychological distress, which was 46% in Thailand and 91% in Egypt. A quarter (24%) had high levels of fear of COVID-19, which was as low as 9% in Libya and as high as 38% in Bangladesh. More than half (57%) exhibited medium to high resilient coping; the lowest prevalence (3%) was reported in Australia and the highest (72%) in Syria. Being female (AOR 1.31 [95% CIs 1.09-1.57]), perceived distress due to change of employment status (1.56 [1.29-1.90]), comorbidity with mental health conditions (3.02 [1.20-7.60]) were associated with higher levels of psychological distress and fear. Doctors had higher psychological distress (1.43 [1.04-1.97]), but low levels of fear of COVID-19 (0.55 [0.41-0.76]); nurses had medium to high resilient coping (1.30 [1.03-1.65])., Conclusions: The extent of psychological distress, fear of COVID-19 and coping varied by country; however, we identified few higher risk groups who were more vulnerable than others. There is an urgent need to prioritise health and well-being of those people through well-designed intervention that may need to be tailored to meet country specific requirements., (© 2021. The Author(s).)

Background: The unprecedented COVID-19 pandemic has resulted in social distancing and isolation which leads to insufficient physical activity and thereby increases sedentary behaviors. Hence, this study aimed to assess the prevalence of insufficient physical activity and sedentary behaviors among medical students during the COVID-19 lockdown in Pakistan, and to determine their associated factors. Methods: A cross-sectional online survey was carried out among 407 medical students from the Punjab and Sindh provinces between May and June 2020. To collect data, an e-questionnaire was sent to obtain informed consent along with questions concerning socio-demographics as well as an International Physical Activity Questionnaires-Short Form (IPAQ-SF). Results: As per the IPAQ, almost five in ten participants were physically inactive (48.2%), and 45.2% reported sedentary behaviors. Participants with insufficient physical activity were more likely to report sedentary behaviors than their counterparts (AOR = 2.53; 95% CI = 1.66-3.85, p < 0.001). The odds of insufficient physical activity were higher among the participants who did not strictly follow the COVID-19 preventive measures (AOR = 2.51; 95% CI = 1.35-4.69, p = 0.004); similarly, there were increased odds of sedentary behaviors observed among participants within a normal weight range compared to those who were underweight (AOR = 2.69; 95% CI = 1.76-4.11, p < 0.001). Conclusions: Insufficient physical activity and sedentary behavior are prevalent among medical students in Pakistan during the COVID-19 pandemic. These findings indicate the importance of establishing tailored policies and programs to encourage young adults to engage in physical activity.

Introduction: The COVID-19 pandemic has enormously affected the psychological well-being, social and working life of millions of people across the world. This study aimed to investigate the psychological distress, fear and coping strategies as a result of the COVID-19 pandemic and its associated factors among Malaysian residents., Methods: Participants were invited to an online cross-sectional survey from Aug-Sep 2020. The study assessed psychological distress using the Kessler Psychological Distress Scale, level of fear using the Fear of COVID-19 Scale, and coping strategies using the Brief Resilient Coping Scale. Univariate and multivariate logistic regression analyses were conducted to adjust for potential confounders., Results: The mean age (±SD) of the participants (N = 720) was 31.7 (±11.5) years, and most of them were females (67.1%). Half of the participants had an income source, while 216 (30%) identified themselves as frontline health or essential service workers. People whose financial situation was impacted due to COVID-19 (AOR 2.16, 95% CIs 1.54-3.03), people who drank alcohol in the last four weeks (3.43, 1.45-8.10), people who were a patient (2.02, 1.39-2.93), and had higher levels of fear of COVID-19 (2.55, 1.70-3.80) were more likely to have higher levels of psychological distress. Participants who self-isolated due to exposure to COVID-19 (3.12, 1.04-9.32) and who had moderate to very high levels of psychological distress (2.56, 1.71-3.83) had higher levels of fear. Participants who provided care to a family member/patient with a suspected case of COVID-19 were more likely to be moderately to highly resilient compared to those who did not., Conclusion: Vulnerable groups of individuals such as patients and those impacted financially during COVID-19 should be supported for their mental wellbeing. Behavioural interventions should be targeted to reduce the impact of alcohol drinking during such crisis period., Competing Interests: The authors have read the journal’s policy and have the following competing interests: RS is a staff member of Telstra Health (https://www.telstrahealth.com/). There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused increasing challenges for healthcare professionals globally. However, there is a dearth of information about these challenges in many developing countries, including Bangladesh. This study aims to explore the challenges faced by healthcare professionals (doctors and nurses) during COVID-19 in Bangladesh. Methods: We conducted qualitative research among healthcare professionals of different hospitals and clinics in Khulna and Dhaka city of Bangladesh from May 2020 to August 2020. We conducted 15 in-depth telephone interviews using a snowball sampling technique. We used an in-depth interview guide as data were collected, audiotaped, and transcribed. The data were analyzed both manually and using QDA Miner software as we used thematic analysis for this study. Results: Seven themes emerged from the study. Participants experienced higher workload, psychological distress, shortage of quality personal protective equipment (PPE), social exclusion/stigmatization, lack of incentives, absence of coordination, and proper management during their service. These healthcare professionals faced difficulty coping with these challenges due to situational and organizational factors. They reported of faith in God and mutual support to be the keys to adapt to adversities. Adequate support to address the difficulties faced by healthcare professionals is necessary for an overall improved health outcome during the pandemic. Conclusion: The findings highlight the common challenges faced by healthcare professionals during the COVID-19 outbreak. This implies the need to support adequate safety kits, protocols, and support for both physical and mental health of the healthcare professionals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Razu, Yasmin, Arif, Islam, Islam, Gesesew and Ward.)

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images., (© 2021. The Author(s).)

Background: The outbreak of Coronavirus disease (COVID-19) caused by a novel coronavirus (named SARS-CoV-2) has gained attention globally and has been recognized as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) due to the rapidly increasing number of deaths and confirmed cases. Health care workers (HCWs) are vulnerable to this crisis as they are the first frontline to receive and manage COVID-19 patients. In this multicenter multinational survey, we aim to assess the level of awareness and preparedness of hospital staff regarding COVID-19 all over the world. Methods: From February to March 2020, the web-based or paper-based survey to gather information about the hospital staff's awareness and preparedness in the participants' countries will be carried out using a structured questionnaire based on the United States Centers for Disease Control and Prevention (CDC) checklist and delivered to participants by the local collaborators for each hospital. As of March 2020, we recruited 374 hospitals from 58 countries that could adhere to this protocol as approved by their Institutional Review Boards (IRB) or Ethics Committees (EC). Discussion: The awareness and preparedness of HCWs against COVID-19 are of utmost importance not only to protect themselves from infection, but also to control the virus transmission in healthcare facilities and to manage the disease, especially in the context of manpower lacking and hospital overload during the pandemic. The results of this survey can be used to inform hospitals about the awareness and preparedness of their health staff regarding COVID-19, so appropriate policies and practice guidelines can be implemented to improve their capabilities of facing this crisis and other future pandemic-prone diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Qarawi, Ng, Gad, Luu, AL-Ahdal, Sharma, Huan, Vuong, Tawfik, Hashan, Dumre, Ghozy, Shaikhkhalil, Mahmoud, Alhady, Nam, Islam, Smith, Lee, Chico, Cox, Hirayama and Huy.)

Study tools examining psychological distress, fear of COVID-19 and coping amongst migrants and non-migrants in Australia are very limited. The aim of this research was to assess the psychometric properties and correlation of the English version of Kessler Psychological Distress Scale (K-10), Fear of COVID-19 Scale (FCSV-19S), and Brief Resilient Coping Scale (BRCS) tools during the COVID-19 pandemic situation in Australia. Data from a cross-sectional survey (n = 516) were utilized to examine reliability; 299 (57.9%) were migrants. High internal consistency, as evidenced by Cronbach's alpha, was found for the K-10 (0.92), FCV-19S (0.87) and BRCS (0.66) tools. The corresponding values for migrants and non-migrants were (0.92, 0.87, 0.67) and (0.92, 0.86, 0.63), respectively. Item-total correlations ranged 0.57-0.78 for K-10, 0.62-0.69 for FCV-19S, and 0.39-0.50 for BRCS tools. EFA retained a single factor for each tool with adequate factor loadings. The scoring of K-10 was significantly predicted by the scoring of FCV-19S (r = 0.284, P < 0.001) and BRCS tool (r = 0.132, P < 0.01). Therefore, these tools can be used reliably amongst both migrant and non-migrant population in Australia., (© 2021 John Wiley & Sons Australia, Ltd.)

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19., (© 2020 Wiley Periodicals LLC.)

Background: Coronavirus disease (COVID-19) pandemic has affected health and lifestyle behaviors of people globally. This project aims to identify the impact of COVID-19 on lifestyle behavior of individuals in the Middle East and North Africa (MENA) region during confinement., Methods: We conducted an online survey in 17 countries (Egypt, Jordan, United Arab Emirates, Kuwait, Bahrain, Saudi Arabia, Oman, Qatar, Yemen, Syria, Palestine, Algeria, Morocco, Libya, Tunisia, Iraq, and Sudan) from the MENA region on August and September 2020. The questionnaire included self-reported information on lifestyle behaviors, including physical activity, eating habits, smoking, watching television, social media use and sleep before and during the pandemic. Logistic regression was performed to analyze the impact of COVID-19 on lifestyle behaviors., Results: A total of 5896 participants were included in the final analysis and 62.8% were females. The BMI of the participants was 25.4 ± 5.8 kg/m 2 . Around 38.4% of the participants stopped practicing any physical activities during the confinement (P < 0.001), and 57.1% reported spending more than 2 h on social media (P < 0.001). There were no significant changes in smoking habits. Also, 30.9% reported an improvement in their eating habits compared with 24.8% reported worsening of their eating habits. Fast-food consumption decreased significantly in 48.8% of the study population. This direct/indirect exposure to COVID-19 was associated with an increased consumption of carbohydrates (OR = 1.09; 95% CI = 1.02-1.17; P = 0.01), egg (OR = 1.08; 95% CI = 1.02-1.16; P = 0.01), sugar (OR = 1.09; 95% CI = 1.02-1.16; P = 0.02), meat, and poultry (OR = 1.13; 95% CI = 1.06-1.20; P < 0.01). There was also associated increase in hours spent on watching television (OR = 1.07; 95% CI = 1.02-1.12; P < 0.01) and social media (OR = 1.09; 95% CI = 1.01-1.18; P = 0.03). However, our results showed a reduction in sleeping hours among those exposed to COVID-19 infection (OR = 0.85; 95% CI = 0.77-0.94; P < 0.01)., Conclusions: The COVID-19 pandemic was associated with an increase in food consumption and sedentary life. Being exposed to COVID-19 by direct infection or through an infected household is a significant predictor of amplifying these changes. Public health interventions are needed to address healthy lifestyle behaviors during and after the COVID-19 pandemic.

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA-DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein-chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein-chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Competing Interests: Competing interests: The authors completed the ICMJE Unified Competing Interest form (available upon request from the corresponding author), and declare no conflicts of interest.

Competing Interests: Competing interests: The authors completed the ICMJE Unified Competing Interest form (available upon request from the corresponding author), and declare no conflicts of interest..

(1) Background: COVID-19 caused the worst international public health crisis, accompanied by major global economic downturns and mass-scale job losses, which impacted the psychosocial wellbeing of the worldwide population, including Saudi Arabia. Evidence of the high-risk groups impacted by the pandemic has been non-existent in Saudi Arabia. Therefore, this study examined factors associated with psychosocial distress, fear of COVID-19 and coping strategies among the general population in Saudi Arabia. (2) Methods: A cross-sectional study was conducted in healthcare and community settings in the Saudi Arabia using an anonymous online questionnaire. The Kessler Psychological Distress Scale (K-10), Fear of COVID-19 Scale (FCV-19S) and Brief Resilient Coping Scale (BRCS) were used to assess psychological distress, fear and coping strategies, respectively. Multivariate logistic regressions were used, and an Adjusted Odds Ratio (AOR) with 95% Confidence Intervals (CIs) was reported. (3) Results: Among 803 participants, 70% (n = 556) were females, and the median age was 27 years; 35% (n = 278) were frontline or essential service workers; and 24% (n = 195) reported comorbid conditions including mental health illness. Of the respondents, 175 (21.8%) and 207 (25.8%) reported high and very high psychological distress, respectively. Factors associated with moderate to high levels of psychological distress were: youth, females, non-Saudi nationals, those experiencing a change in employment or a negative financial impact, having comorbidities, and current smoking. A high level of fear was reported by 89 participants (11.1%), and this was associated with being ex-smokers (3.72, 1.14–12.14, 0.029) and changes in employment (3.42, 1.91–6.11, 0.000). A high resilience was reported by 115 participants (14.3%), and 333 participants (41.5%) had medium resilience. Financial impact and contact with known/suspected cases (1.63, 1.12–2.38, 0.011) were associated with low, medium, to high resilient coping. (4) Conclusions: People in Saudi Arabia were at a higher risk of psychosocial distress along with medium-high resilience during the COVID-19 pandemic, warranting urgent attention from healthcare providers and policymakers to provide specific mental health support strategies for their current wellbeing and to avoid a post-pandemic mental health crisis. [ABSTRACT FROM AUTHOR]

Background and Aims: The COVID‐19 pandemic and the resultant change in sedentary behaviors have had immense health, economic, and social implications globally. As governments worldwide imposed lockdowns and curfews, the amount of time spent indoors greatly increased. This lead to a dramatic change in physical activity (PA) levels and profound consequences on daily routines. Our study aimed to investigate patterns of PA during the COVID‐19 pandemic among adults residing in Saudi Arabia. Methods: This cross‐sectional survey‐based study aimed to investigate patterns of PA during the COVID‐19 pandemic among adults residing in Saudi Arabia. The International Physical Activity Questionnaire was utilized to measure participants' PA levels between April 2021 and May 2021. Participants were then classified into three groups according to their PA level, and their PA levels and sedentary behaviors were analyzed. Results: We surveyed 463 participants, 315 (68%) of which were female and 134 (32%) of which were male with a median age of 23 (interquartile range, 21–35) years. Moderate‐to‐high PA was reported by 257 (55.7%) of the participants. There was a significant decrease in PA during the COVID‐19 pandemic and resultant lockdowns among the participants (p = 0.04), with higher rates of sedentary behavior among males than females (p = 0.14). Conclusions: The decline in PA is a profound challenge of the COVID‐19 pandemic that needs to be addressed by health practitioners and policymakers. Our study highlights the decline in PA levels seen during the COVID‐19 pandemic and the importance of promotional programs and interventions to increase PA among the Saudi Arabian population without compromising the essential health restrictions and social distancing. [ABSTRACT FROM AUTHOR]

COVID-19 vaccines are crucial to control the pandemic and avoid COVID-19 severe infections. The rapid evolution of COVID-19 variants such as B.1.1.529 is alarming, especially with the gradual decrease in serum antibody levels in vaccinated individuals. Middle Eastern countries were less likely to accept the initial doses of vaccines. This study was directed to determine COVID-19 vaccine booster acceptance and its associated factors in the general population in the MENA region to attain public herd immunity. We conducted an online survey in five countries (Egypt, Iraq, Palestine, Saudi Arabia, and Sudan) in November and December 2021. The questionnaire included self-reported information about the vaccine type, side effects, fear level, and several demographic factors. Kruskal–Wallis ANOVA was used to associate the fear level with the type of COVID-19 vaccine. Logistic regression was performed to confirm the results and reported as odds ratios (ORs) and 95% confidence intervals. The final analysis included 3041 fully vaccinated participants. Overall, 60.2% of the respondents reported willingness to receive the COVID-19 booster dose, while 20.4% were hesitant. Safety uncertainties and opinions that the booster dose is not necessary were the primary reasons for refusing the booster dose. The willingness to receive the booster dose was in a triangular relationship with the side effects of first and second doses and the fear (p < 0.0001). Females, individuals with normal body mass index, history of COVID-19 infection, and influenza-unvaccinated individuals were significantly associated with declining the booster dose. Higher fear levels were observed in females, rural citizens, and chronic and immunosuppressed patients. Our results suggest that vaccine hesitancy and fear in several highlighted groups continue to be challenges for healthcare providers, necessitating public health intervention, prioritizing the need for targeted awareness campaigns, and facilitating the spread of evidence-based scientific communication. [ABSTRACT FROM AUTHOR]

Background: Symptoms of the novel coronavirus disease (COVD-19) are well known, although asymptomatic cases were also reported due to this rapidly evolving viral disease. However, there has been limited research with inconsistent findings on symptoms of COVID-19 and disease severity. We aimed to evaluate the association between symptoms and severity of disease in adult patients with confirmed COVID-19 by performing a meta-analysis. Methods: We conducted this study by searching four online databases (Medline, Web of Science, EMBASE and Cochrane library) of published studies that included symptoms of COVID-19 cases and severity of the disease between January 1, 2020, and October 31, 2021. PRISMA and MOOSE guidelines were followed, and only articles published in English were selected. We performed meta-analysis using Mantel-Haenszel random-effects model. Note that we included peer-reviewed studies conducted in Wuhan and published in the English language that reported the clinical characteristics of COVID-19, particularly the symptoms of novel coronavirus patients with their prevalence and distribution of patients based on the severity of the disease. Results: Out of 255 articles identified, a total of twenty articles, including 5390 participants, met the inclusion criteria and were included. Among the participants, 2997 (55.60%) were males, and 974 (18.07%) reported severe conditions. Fever was the most commonly reported symptom in the reported COVID-19 confirmed cases (88.47%, 95% CI: 80.74– 93.35%), which was followed by cough, fatigue, and less proportionally dyspnea and myalgia. Dyspnea was the only symptom, which was associated with severity of COVID-19 (OR 2.43, 95% CI: 1.52– 3.89). Conclusion: Dyspnoea was found to be associated with severity of COVID-19. People with existing respiratory illnesses, such as chronic obstructive pulmonary diseases need to be careful about the onset of such symptom and should seek medical attention. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has imposed psychological distress and fear across the globe; however, factors associated with those issues or the ways people cope may vary by country or context. This study aimed to investigate the factors associated with psychological distress, fear, and coping strategies for people living in Bangladesh during the COVID-19 pandemic. Methods: A cross-sectional study conducted in August-September 2020 using online platforms in Bangladesh. People residing in Bangladesh, aged ≥18 years, who were proficient in English and able to respond to online questionnaire. The Kessler Psychological Distress Scale was used to assess the psychological stress. Level of fear was assessed using the Fear of COVID-19 Scale, and strategies to cope were assessed using the Brief Resilient Coping Scale. Results: Of the 962 participants, half of them were aged between 30 and 59 years. Being born in Bangladesh, having graduate education, perceived distress due to employment change, effect of COVID-19 on financial situation, having multiple comorbidities, and visiting a healthcare provider in the last 4 weeks were associated with higher levels of both psychological distress and fear of COVID-19. Furthermore, higher psychological distress was associated with being a female (AOR 1.81, 95% CI 1.33–2.47, p < 0.001), being a frontline worker (AOR 1.50, 95% CI 1.04–2.15, p < 0.05), having pre-existing psychiatric problems (AOR 4.03, 95% CI 1.19–13.7, p < 0.05), being a smoker (AOR 2.02, 95% CI 1.32–3.09, p < 0.01), providing care to a known/suspected COVID-19 patient (AOR 1.96, 95% CI 1.40–2.72, p < 0.001), having a recent overseas travel history and being in self-quarantine (AOR 4.59, 95% CI 1.23–17.2, p < 0.05), self-isolation without COVID-19 (AOR 2.63, 95% CI 1.68–4.13, p < 0.001) or being COVID-19 positive (AOR 2.53, 95% CI 1.19–5.34, p < 0.05), and having high levels of fear of COVID-19 (AOR 3.27, 95% CI 2.29–4.66, p < 0.001). A higher level of fear was associated with moderate to high levels of psychological distress (AOR 3.29, 95% CI 2.31–4.69, p < 0.001). People with pre-existing mental health problems were less likely to be resilient (AOR 0.25, 95% CI 0.11–0.54, p < 0.01), whereas those with having an income were more likely to be resilient (AOR 1.46, 95% CI 1.02–2.11, p < 0.05). Conclusion: Effective interventions to support the vulnerable groups including improved access to mental health services are of utmost importance during the pandemic. [ABSTRACT FROM AUTHOR]

• COVID-19 related lockdown policies and stay at home strategy may lead to the overuse or excessive usage of smartphones. • Excessive use of smartphones can potentially lead to gaming disorders and internet use disorders and eventually be considered to cause psychosocial crisis. • Without proper initiative, problematic smartphone usage can turn into an emerging public health challenge to annihilate lives by perpetuating the socio-psychological problems. • It will be a crucial step to monitor smartphone overuse and take necessary action to minimise the problem through protective policies and family support during and after this COVID 19 pandemic. [ABSTRACT FROM AUTHOR]

• We ourselves collected the data. So, the dataset is new. • Proposed method was tested on six public datasets and the results are better than most of state of the art papers. • We showed that using the Sobel filter can improve the performance of CNN. • We did not use pre-trained networks unlike most of the other researches. We ourselves designed the network. • Our method performance was better than the compared references. The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. [ABSTRACT FROM AUTHOR]