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Coronavirus disease 2019 (COVID-19) is a severe and infectious respiratory condition caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This case-control study aimed to evaluate serum levels of various immunological markers in patients with COVID-19 compared to those with bacterial pneumonia and a healthy control group. Serum samples were collected from adult participants across various COVID-19 isolation centers, including Kassala State and Ahmed Gasim Hospital, between April and June 2021. The study included 70 patients diagnosed with COVID-19, 30 with bacterial pneumonia, and 50 healthy controls. Serum levels of C-reactive protein (CRP), complement components C3 and C4, and cytokines IL-8, IL-10, IL-12, TNF-α, and IFN-γ were measured using standard reagent kits. Serum level of CRP was significantly elevated in both bacterial pneumonia and COVID-19 but significantly higher among patients with bacterial pneumonia. C3 and C4 were also increased in both patient groups, with C3 significantly higher in bacterial pneumonia. IL-8, IL-10, IL-12, TNF-α, and IFN-γ were significantly increased in bacterial pneumonia and SARS-Cov-2 compared to healthy controls. However, IFN-γ was significantly increased among patients with COVID-19 than patients with bacterial pneumonia. This study highlights the potential significant impact of COVID-19 on the immunological biomarkers investigated., Competing Interests: The authors declare no conflict of interest., (© 2023 The Author(s).)

Background: Burnout and depression among health professions education (HPE) students continue to rise, leading to unwanted effects that ultimately jeopardise optimal medical care and patient health. Promoting the resilience of medical students is one solution to this issue. Several interventions have been implemented to foster resilience, but they focus on aspects other than the primary cause: the assessment system. The purpose of this study is to develop a framework to promote resilience in assessment planning and practice., Methods: We followed the guidelines suggested by Whetten for constructing a theoretical model for framework development. There were four phases in the model development. In the first phase, different literature review methods were used, and additional students' perspectives were collected through focus group discussions. Then, using the data, we constructed the theoretical model in the second phase. In the third phase, we validated the newly developed model and its related guidelines. Finally, we performed response process validation of the model with a group of medical teachers., Results: The developed systematic assessment resilience framework (SAR) promotes four constructs: self-control, management, engagement, and growth, through five phases of assessment: assessment experience, assessment direction, assessment preparation, examiner focus, and student reflection. Each phase contains a number of practical guidelines to promote resilience. We rigorously triangulated each approach with its theoretical foundations and evaluated it on the basis of its content and process. The model showed high levels of content and face validity., Conclusions: The SAR model offers a novel guideline for fostering resilience through assessment planning and practice. It includes a number of attainable and practical guidelines for enhancing resilience. In addition, it opens a new horizon for HPE students' future use of this framework in the new normal condition (post COVID 19)., (© 2023. The Author(s).)

The coronavirus disease 2019 (Covid-19) pandemic caused an abrupt transition from face-to-face to online anatomy teaching, learning, and assessment. Although online education has ensured the continuity of anatomy education during the pandemic, its implementation has been challenging, and its effectiveness has been questioned. Therefore, literature pertinent to online anatomy education during the pandemic is crucial to explain Covid-19's disruptions to this field. Accordingly, this scoping review explored changes, disruptions, and gaps in anatomy teaching and assessment during Covid-19 using an enhanced version of Arksey and O'Malley's six-stage protocol. Five online databases were searched for articles that described changes and disruptions in anatomy education. Three independent researchers were involved in titles, abstracts, and full texts screening, while another four researchers were independently involved in data extraction, charting, and synthesis. This review revealed six themes: immediate strategic plans and actions, teaching and learning changes, online assessment practice, students' and educators' receptivity and adaptability, online learning and assessment effects, and future directions. It also revealed four gaps: non-future-ready curricula, learning obstacles, administrative and teaching challenges, and online education ethical issues. The results were reported in tabular and narrative forms, following the PRISMA Extension for Scoping Reviews (PRISMA-ScR guidelines). Understanding the evolution and gaps in anatomy education during the Covid-19 pandemic will help anatomists design future-ready, adaptable curricula., (© 2022 American Association for Anatomy.)

Objective: This study aims to comprehensively evaluate olfactory and gustatory dysfunctions during the COVID-19 pandemic regarding onset, course, associated symptoms, prognosis and relation to patients' demographics, treatment received and other symptoms., Patients& Methods: This is a prospective study conducted on patients proven to be infected with COVID-19 and with olfactory/gustatory dysfunction symptoms. Detailed history was taken from each patient about the onset of this dysfunction, associated symptoms. Then follow-up survey was done after 6 months to evaluate the prognosis., Results: 1031 patients were included in the study, aged 18 to 69 years old, with 31.8% were male. Olfactory/gustatory dysfunctions occurred after other COVID-19 symptoms in 43.5% of cases, occurred suddenly in 80.4% and gradually in 19.6%. These dysfunctions were anosmia & ageusia in 50.2%, hyposmia & hypogeusia in 23.3%, anosmia alone in 17.7%, phantosmia in 18%, Parosmia in 28.4%. In terms of recovery 6-month follow up, 680 patients (66%) recovered completely, 22.1% recovered partially while 11.9% did not recover. Most improvement occurred in the first two weeks. Headache, malaise, nasal obstruction and rhinorrhea were the commonest COVID-19 symptoms associated., Conclusion: Most recovery of olfactory/gustatory dysfunction in COVID-19 infection occurs at the first two weeks and is unrelated to patient demographics, treatment or olfactory training. Parosmia is an independent predictor for complete recovery, while phantosmia is significantly associated with lower probability of complete recovery., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Online learning has become the new normal in many medical and health science schools worldwide, courtesy of COVID-19. Satisfaction with online learning is a significant aspect of promoting successful educational processes. This study aimed to identify factors affecting student and faculty satisfaction with online learning during the new normal. Online questionnaires were emailed to students (n = 370) and faculty (n = 81) involved in online learning during the pandemic. The questionnaires included closed- and open-ended questions and were organised into two parts: socio-demographic information and satisfaction with online learning. Descriptive statistics were used to analyse the responses to the satisfaction scales. Students' and faculty responses to the open-ended questions were analysed using the thematic analysis method. The response rate was 97.8% for students and 86.4% for faculty. Overall satisfaction among students was 41.3% compared to 74.3% for faculty. The highest areas of satisfaction for students were communication and flexibility, whereas 92.9% of faculty were satisfied with students' enthusiasm for online learning. Technical problems led to reduced student satisfaction, while faculty were hampered by the higher workload and the required time to prepare the teaching and assessment materials. Study-load and workload, enhancing engagement, and technical issues (SWEET) were the themes that emerged from the thematic analysis as affecting student and faculty satisfaction. Adopting a combination synchronous and asynchronous approach, incorporating different applications to engage students, and timely feedback are imperative to increasing student satisfaction, while institutional support and organisational policy could enhance faculty satisfaction.

The novel coronavirus disease that arises in the end of 2019 (COVID-19) in Wuhan, China, has rapidly spread over the globe and was considered as a world pandemic. Currently, various antiviral therapies or vaccines are available, and many researches are ongoing for further treatments. Targeting the coronavirus' main protease (key enzyme: 3CLpro) is growing in importance in anti-SARS-CoV-2 drug discovery process. The present study aims at predicting the antiviral activity of two novel compounds using in silico approaches that might become potential leads against SARS-CoV-2. The 3D structures of the new compounds are elucidated by single-crystal X-ray techniques. The interactions between different units of 4 and 5 were emphasized by analyzing their corresponding Hirshfeld surfaces and ESP plots. NBO and FMO analyses were investigated as well. Molecular docking combined with molecular dynamics simulations (MDs) was performed to investigate the binding modes and molecular interactions of 4 and 5 with the amino acids of coronavirus main protease (6LU7) protein. The best docking scores were obtained for both ligands through the major binding interactions via hydrogen/hydrophobic bonds with the key amino acids in the active site: HIS41, CYS145, MET49, MET165, HIS172, and GLU166 amino acids. A MD simulation study was also performed for 100 ns to validate the stability behavior of the main protease 3CLpro-ligand complexes. The MD simulation study successfully confirmed the stability of the ligands in the binding site as potent anti-SARS-CoV-2 (COVID-19) inhibitors. Additionally, MMPBSA energy of both docked complexes was determined as a validation assay of docking and MD simulations to validate compound conformation and interaction stability with 3CLpro. The synthesized compounds might be helpful in the fight against COVID-19 prior to biological activity confirmation in vitro and in vivo., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Importance: Bitter taste receptors (T2Rs) have been implicated in sinonasal innate immunity, and genetic variation conferred by allelic variants in T2R genes is associated with variation in upper respiratory tract pathogen susceptibility, symptoms, and outcomes. Bitter taste receptor phenotype appears to be associated with the clinical course and symptom duration of SARS-CoV-2 infection., Objective: To evaluate the association between T2R phenotype and patient clinical course after infection with SARS-CoV-2., Design, Setting, and Participants: A prospective cohort study was performed from July 1 through September 30, 2020, at a tertiary outpatient clinical practice and inpatient hospital in the United States among 1935 participants (patients and health care workers) with occupational exposure to SARS-CoV-2., Exposure: Exposure to SARS-CoV-2., Main Outcomes and Measures: Participants underwent T2R38 phenotype taste testing to determine whether they were supertasters (those who experienced greater intensity of bitter tastes), tasters, or nontasters (those who experienced low intensity of bitter tastes or no bitter tastes) and underwent evaluation for lack of infection with SARS-CoV-2 via polymerase chain reaction (PCR) testing and IgM and IgG testing. A group of participants was randomly selected for genotype analysis to correlate phenotype. Participants were followed up until confirmation of infection with SARS-CoV-2 via PCR testing. Phenotype of T2R38 was retested after infection with SARS-CoV-2. The results were compared with clinical course., Results: A total of 1935 individuals (1101 women [56.9%]; mean [SD] age, 45.5 [13.9] years) participated in the study. Results of phenotype taste testing showed that 508 (26.3%) were supertasters, 917 (47.4%) were tasters, and 510 (26.4%) were nontasters. A total of 266 participants (13.7%) had positive PCR test results for SARS-CoV-2. Of these, 55 (20.7%) required hospitalization. Symptom duration among patients with positive results ranged from 0 to 48 days. Nontasters were significantly more likely than tasters and supertasters to test positive for SARS-CoV-2 (odds ratio, 10.1 [95% CI, 5.8-17.8]; P < .001), to be hospitalized once infected (odds ratio, 3.9 [1.5-10.2]; P = .006), and to be symptomatic for a longer duration (mean [SE] duration, 23.7 [0.5] days vs 13.5 [0.4] days vs 5.0 [0.6] days; P < .001). A total of 47 of 55 patients (85.5%) with COVID-19 who required inpatient admission were nontasters. Conversely, 15 of 266 patients (5.6%) with positive PCR test results were supertasters., Conclusions and Relevance: This cohort study suggests that T2R38 receptor allelic variants were associated with participants' innate immune response toward SARS-CoV-2. The T2R phenotype was associated with patients' clinical course after SARS-CoV-2 infection. Nontasters were more likely to be infected with SARS-CoV-2 than the other 2 groups, suggesting enhanced innate immune protection against SARS-CoV-2.

The relationship between journalists and news source has been challenged by new digital technologies, the crisis in public communication and the multiplication of sources of information and news, and the COVID-19 pandemic, which has transformed journalistic routines and audience behaviour. Existing research on news sources has contributed to our understanding of which voices dominate the news agenda in normal conditions, but there remains little research on the news sources and journalists’ relationship during crisis, particularly in non-Western contexts. In this paper, we seek to partially address the lacuna, drawing on semi-structured interviews with selected journalists from across the Arab world to address the effect of COVID-19 on journalists’ relationship with their news sources and what we call source cultivation practices in a region where traditional forms of personal networking as well as cultural norms have defined the relationship between journalists and news sources in the legacy media. In addressing these issues, this paper contributes to research on news sources during crises while drawing attention to the theoretical and methodological gaps in news sources research in the region as well as the continued dominance of Western- and media-centric paradigms in the broad field of the sociology of news production. [ABSTRACT FROM AUTHOR]

During the epidemic of COVID‐19, Computed Tomography (CT) is used to help in the diagnosis of patients. Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, particularly while radiologists are fighting the coronavirus disease. It is challenging to equate these techniques since they were built on separate datasets, educated on various training sets, and tested using different metrics. In this research, a deep learning semantic segmentation architecture for COVID‐19 lesions detection in limited chest CT datasets will be presented. The proposed model architecture consists of the encoder and the decoder components. The encoder component contains three layers of convolution and pooling, while the decoder contains three layers of deconvolutional and upsampling. The dataset consists of 20 CT scans of lungs belongs to 20 patients from two sources of data. The total number of images in the dataset is 3520 CT scans with its labelled images. The dataset is split into 70% for the training phase and 30% for the testing phase. Images of the dataset are passed through the pre‐processing phase to be resized and normalized. Five experimental trials are conducted through the research with different images selected for the training and the testing phases for every trial. The proposed model achieves 0.993 in the global accuracy, and 0.987, 0.799, 0.874 for weighted IoU, mean IoU and mean BF score accordingly. The performance metrics such as precision, sensitivity, specificity and F1 score strengthens the obtained results. The proposed model outperforms the related works which use the same dataset in terms of performance and IoU metrics. [ABSTRACT FROM AUTHOR]

Anatomy was studied through e-learning as a social distancing measure to prevent the spread of COVID-19. This study explored anatomy educators' and learners' experiences with online learning in two Middle Eastern countries in terms of instruction, engagement and assessment. The study employed a cross-sectional, mixed-methods approach using a validated questionnaire to collect data from students in 14 medical schools in Saudi Arabia and the United Arab Emirates on their experience with online teaching, engagement strategies and assessment in anatomy courses. The Delphi technique was used to explore faculty challenges and recommended solutions. Six hundred and sixtysix students completed the questionnaire. Less than half (41.74%) reported that their interaction with the instructor was easier and more comfortable than during face-to-face lectures. In addition, less than a quarter (21.32%) believed it was better to adopt online rather than face-to-face anatomy instruction. Faculty members described challenges and provided suggestions to enhance online teaching, including faculty and staff development, technical support, appropriate software to increase student engagement and curriculum development to suit the new normal. They also suggested improving assessment design and construction as well as adopting measures to eliminate cheating, train faculty and staff and properly select software. In conclusion, the student and faculty experiences with e-learning in anatomy were generally positive. Both faculty and staff identified many challenges with an emphasis on the loss of face-to-face teaching. Accordingly, faculty development, technical support, appropriate software to enhance student engagement and reformed curricula to suit online teaching are needed in online anatomy education. [ABSTRACT FROM AUTHOR]

COVID-19's fast spread in 2020 compelled the World Health Organization (WHO) to declare COVID-19 a worldwide pandemic. According to the WHO, one of the preventative countermeasures against this type of virus is to use face masks in public places. This paper proposes a face mask detection model by extracting features based on the neutrosophic RGB with deep transfer learning. The suggested model is divided into three steps, the first step is the conversion to the neutrosophic RGB domain. This work is considered one of the first trails of applying neutrosophic RGB conversion to image domain, as it was commonly used in the conversion of grayscale images. The second step is the feature extraction using Alexnet, which has been small number of layers. The detection model is created in the third step using two traditional machine learning algorithms: decision trees classifier and Support Vector Machine (SVM). The Simulated Masked Face dataset (SMF) and the Real-World Mask Face dataset (RMF) are merged to a single dataset with two categories (a face with a mask, and a face without a mask). According to the experimental results, the SVM classifier with the True (T) neutrosophic domain achieved the highest testing accuracy with 98.37%. [ABSTRACT FROM AUTHOR]

Background: Fear of infection, the epidemic situation, unexpected lockdown, and implementation of online classes are most likely affecting the psychological well-being of students during the COVID-19 pandemic. Therefore, this study aims to assess the level of knowledge, anxiety, and psychological distress concerning COVID-19 and their association with fear, gender, age, history of mental illness, time spent reading about COVID-19, program of study, and type of dwelling among students in the United Arab Emirates (UAE). Methods and Materials: In this cross-sectional study, 433 students participated in a web-based survey. These were students at the University of Sharjah, coming from all the emirates of the UAE. Demographic scale, COVID-19 knowledge, anxiety, fear, and psychological distress scales were used to screen these problems. Results: Of the 433 students, 278 (64.2%) were male and 155 (35.8%) were female. Overall, 353 (81.5%) exhibited adequate knowledge of COVID-19. Sixty-nine (15.9%) of students were anxious and 221 (51%) were in psychological distress. Students who exhibited anxiety concerning COVID-19 anxiety (odds ratio [OR]: 2.98) and fear (OR: 1.27), and who spent more than 4 h reading about COVID-19 (OR: 11.20) were more psychologically distressed. Students with a history of mental illness showed adequate knowledge of COVID-19; however, they were more psychologically distressed (OR: 5.93). Older students were less likely to have psychological distress (OR: 0.87). Conclusion: Students possess adequate knowledge concerning COVID-19; however, they are psychologically distressed. Age, dwelling status, history of mental illness, anxiety, and fear significantly predicted psychological distress. Frequent web-based workshops that include insight, guidance, online counseling, scheduled activity, and coping mechanisms for COVID-19 are highly recommended. The authors discuss the implications for future research and provide recommendations for students and educational institutions. [ABSTRACT FROM AUTHOR]

Introduction: SARS-CoV-2 has been identified as the pathogen causing the outbreak of Coronavirus Disease 2019 (COVID-19) that started in Wuhan, China, in December 2019. SARS-CoV-2 has human-to-human transmission ability and universally contagious to all populations. The main transmission patterns are respiratory droplets transmission and contact transmission. The purpose of this study is to propose a protocol that may be used as a guide to reduce the incidence of COVID-19 infections among otolaryngology care teams. Methods: A prospective cohort study was conducted to show the efficacy of our protocol to prevent transmission to health-care providers from March 11, 2020 through April 14, 2020. The protocol consisted of a series of protective measures that we applied to all health-care providers, then testing of our providers for COVID-19 using reverse transcription polymerase chain reaction along with immunoglobulin M (IgM) and immunoglobulin G (IgG) testing at the end of the study period to ensure effectiveness. Results: Our protocol resulted in zero transmissions to our health-care providers during the duration of the initial study. We were involved in greater than 150 sinonasal, skull base, open airway, and endoscopy procedures during this study. At the conclusion of the initial 5 weeks, we had no health-care providers test positive for SARS-CoV-2. Conclusion: According to our proposed protocol, we were able to provide care for all patients in clinic, hospital, emergent, intensive, and surgical settings with no transmission of SARS-CoV-2 by symptomatology and post evaluation testing. [ABSTRACT FROM AUTHOR]

Background: Severe acute respiratory syndrome‒associated coronavirus‐2 (SARS‐CoV‐2) has been identified as the pathogen causing the outbreak of coronavirus disease‐2019 (COVID‐19) commencing in Wuhan, China, in December 2019. Multiple reports have shown subjective loss of taste and smell as an early and hallmark symptom for COVID‐19. Methods: A retrospective study was performed in our clinical practice during July 2020 on patients positive for SARS‐CoV‐2 via polymerase chain reaction. All patients were categorized into 3 groups (supertasters, tasters, and nontasters) via taste sensitivity to phenylthiocarbamide, thiourea, and sodium benzoate with taste strip testing. The results of the taste strip tests were correlated with clinical course. Results: A total of 100 patients (mean, 51 [range, 24‐82] years of age; 44 [44%] women) were assessed. We found that 21 of 100 (21%) were nontasters, 79 of 100 (79%) were tasters, and 0 of 100 (0%) were supertasters (p < 0.001). Twenty‐one of 21 (100%) (p < 0.001) of the patients requiring inpatient admission were classified as nontasters. All 79 (100%) (p < 0.001) of the patients who displayed mild to moderate symptoms not requiring admission were classified as tasters. Conclusion: Our results show objective data that taste disturbance, specifically global loss of taste, appears to correlate with the clinical course specific to each individual, because 100% of the patients requiring inpatient admission were classified as nontasters. [ABSTRACT FROM AUTHOR]

COVID-19 has become a global pandemic of the highest priority. Multiple treatment protocols have been proposed worldwide with no definitive answer for acure. A prior retrospective study showed association between bitter taste receptor 38 (T2R38) phenotypes and the severity of COVID-19. Based on this, we proposed assessing the different T2R38 phenotypes response towards a targeted treatment protocol. Starting July 2020 till December 2020, we tested subjects for T2R38 phenotypic expression (supertasters, tasters, and nontasters). Subjects who were subsequently infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (diagnosed via PCR) were included. Based on their taster status, supertasters were given dexamethasone for 4 days; tasters were given azithromycin and dexamethasone +/− hydroxychloroquine for 7 days; and nontasters were given azithromycin and dexamethasone for 12 days. Subjects were followed prospectively and their outcomes were documented. Seven hundred forty-seven COVID-19 patients were included, with 184 (24.7%) supertasters, 371 (49.6%) tasters, and192 (25.7%) nontasters. The average duration of symptoms with the treatment protocol was 5 days for supertasters, 8.1 days for tasters, and 16.2 days for nontasters. Only three subjects (0.4%) required hospitalization (3/3 nontasters). Targeted treatment protocol showed significant correlation (p < 0.05) based on patients' T2R38 phenotypic expression. Assessing treatment protocols for COVID-19 patients according to their T2R38 phenotype could provide insight into the inconsistent results obtained from the different studies worldwide. Further study is warranted on the categorization of patients based on their T2R38 phenotype. [ABSTRACT FROM AUTHOR]

• A novel deep learning model for medical face mask detection. • The model can help governments to prevent the COVID-19 transmission. • Two medical face mask datasets have been tested. • The YOLO-v2 with ResNet-50 model achieves high average precision. Deep learning has shown tremendous potential in many real-life applications in different domains. One of these potentials is object detection. Recent object detection which is based on deep learning models has achieved promising results concerning the finding of an object in images. The objective of this paper is to annotate and localize the medical face mask objects in real-life images. Wearing a medical face mask in public areas, protect people from COVID-19 transmission among them. The proposed model consists of two components. The first component is designed for the feature extraction process based on the ResNet-50 deep transfer learning model. While the second component is designed for the detection of medical face masks based on YOLO v2. Two medical face masks datasets have been combined in one dataset to be investigated through this research. To improve the object detection process, mean IoU has been used to estimate the best number of anchor boxes. The achieved results concluded that the adam optimizer achieved the highest average precision percentage of 81% as a detector. Finally, a comparative result with related work has been presented at the end of the research. The proposed detector achieved higher accuracy and precision than the related work. [ABSTRACT FROM AUTHOR]

• A hybrid deep and machine learning model proposed for face mask detection. • The model can impede the Coronavirus transmission, specially COVID-19. • Three face mask datasets have experimented with this research. • The introduced model achieves high performance in the experimental study. The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy. [ABSTRACT FROM AUTHOR]