ABSTRACT
Mucormycosis (MCM) is a rare fungal disorder that has recently been increased in parallel with novel COVID-19 infection. MCM with COVID-19 is extremely lethal, particularly in immunocompromised individuals. The collection of available scientific information helps in the management of this co-infection, but still, the main question on COVID-19, whether it is occasional, participatory, concurrent, or coincidental needs to be addressed. Several case reports of these co-infections have been explained as causal associations, but the direct contribution in immunocompromised individuals remains to be explored completely. This review aims to provide an update that serves as a guide for the diagnosis and treatment of MCM patients' co-infection with COVID-19. The initial report has suggested that COVID-19 patients might be susceptible to developing invasive fungal infections by different species, including MCM as a co-infection. In spite of this, co-infection has been explored only in severe cases with common triangles: diabetes, diabetes ketoacidosis, and corticosteroids. Pathogenic mechanisms in the aggressiveness of MCM infection involves the reduction of phagocytic activity, attainable quantities of ferritin attributed with transferrin in diabetic ketoacidosis, and fungal heme oxygenase, which enhances iron absorption for its metabolism. Therefore, severe COVID-19 cases are associated with increased risk factors of invasive fungal co-infections. In addition, COVID-19 infection leads to reduction in cluster of differentiation, especially CD4+ and CD8+ T cell counts, which may be highly implicated in fungal co-infections. Thus, the progress in MCM management is dependent on a different strategy, including reduction or stopping of implicit predisposing factors, early intake of active antifungal drugs at appropriate doses, and complete elimination via surgical debridement of infected tissues.
ABSTRACT
Background: The coronavirus disease-2019 (COVID-19) is a global public health disaster imposing a nationwide lockdown. This study was undertaken to determine the impact of COVID-19 quarantine on physical, nutritional, psychosocial life, and work aspects on the population of Saudi Arabia. Methods: Data collection was based on the fear of COVID-19 Scale (FCV-19S) and was analyzed by the Likert-type scale. A total of 2828 individuals participated during their COVID-19 quarantine. The data were collected during June 10-17, 2020 using the psychosocial FCV-19S. Results: COVID-19 quarantine was negatively correlated with the physical, nutritional, psychosocial life and work aspects of the Saudi Arabia's population (P < 0.05). As a result of the correlation analysis, gender, sociodemographic status and having a family member dying of COVID-19, marital status (single), monthly income (<3000) and occupation (student), and lost a job or businesses were significantly associated with fear of COVID-19 (P < 0.05). Furthermore, the participants reported a reduction in their physical activity by 59%, whereas 26.5% of participants showed an increase of body weight. Moreover, 23% of participants lost their jobs during the pandemic. Conclusions: The lockdown period was associated with an increase in the COVID-19 fear score. The degree FCV-19S was varied in different categories in several aspects. Low levels of physical activity and weight gained were observed during the lockdown period.
ABSTRACT
The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.
ABSTRACT
BACKGROUND: The COVID-19 pandemic has enhanced the adoption of virtual learning after the urgent suspension of traditional teaching. Different online learning strategies were established to face this learning crisis. The present descriptive cross-sectional study was conducted to reveal the different digital procedures implemented by the College of Medicine at Qassim University for better student performance and achievement. METHODS: The switch into distance-based learning was managed by the digitalization committee. Multiple online workshops were conducted to the staff and students about the value and procedures of such a shift. New procedures for online problem-based learning (PBL) sessions were designed. Students' satisfaction was recorded regarding the efficiency of live streaming educational activities and online assessment. RESULTS: The students were satisfied with the overall shift into this collaborative e-learning environment and the new successful procedures of virtual PBL sessions. The digital learning tools facilitated the performance of the students and their peer sharing of knowledge. The role of informatics computer technologies was evident in promoting the students, research skills, and technical competencies. CONCLUSIONS: The present work elaborated on the procedures and privileges of the transformation into digitalized learning, particularly the PBL sessions, which were appreciated by the students and staff. It recommended the adoption of future online theoretical courses as well as the development of informatics computer technologies.