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Abstract In the current report, we studied the possible inhibitors of COVID-19 from bioactive constituents of Centaurea jacea using a threefold approach consisting of quantum chemical, molecular docking and molecular dynamic techniques. Centaurea jacea is a perennial herb often used in folk medicines of dermatological complaints and fever. Moreover, anticancer, antioxidant, antibacterial and antiviral properties of its bioactive compounds are also reported. The Mpro (Main proteases) was docked with different compounds of Centaurea jacea through molecular docking. All the studied compounds including apigenin, axillarin, Centaureidin, Cirsiliol, Eupatorin and Isokaempferide, show suitable binding affinities to the binding site of SARS-CoV-2 main protease with their binding energies -6.7 kcal/mol, -7.4 kcal/mol, -7.0 kcal/mol, -5.8 kcal/mol, -6.2 kcal/mol and -6.8 kcal/mol, respectively. Among all studied compounds, axillarin was found to have maximum inhibitor efficiency followed by Centaureidin, Isokaempferide, Apigenin, Eupatorin and Cirsiliol. Our results suggested that axillarin binds with the most crucial catalytic residues CYS145 and HIS41 of the Mpro, moreover axillarin shows 5 hydrogen bond interactions and 5 hydrophobic interactions with various residues of Mpro. Furthermore, the molecular dynamic calculations over 60 ns (6×106 femtosecond) time scale also shown significant insights into the binding effects of axillarin with Mpro of SARS-CoV-2 by imitating protein like aqueous environment. From molecular dynamic calculations, the RMSD and RMSF computations indicate the stability and dynamics of the best docked complex in aqueous environment. The ADME properties and toxicity prediction analysis of axillarin also recommended it as safe drug candidate. Further, in vivo and in vitro investigations are essential to ensure the anti SARS-CoV-2 activity of all bioactive compounds particularly axillarin to encourage preventive use of Centaurea jacea against COVID-19 infections.

The privacy and confidentiality of our data are more fundamental to our existence than ever, especially with our growing dependence on online services and the transfer of information, be it for convenience or the unfortunate COVID 19 pandemic. Traditional security measures such as cryptography and steganography are essential. However, they are not sufficient anymore as when one party maintains complete control over the data, they may intentionally, or not, lose or reveal it. Thus, secret sharing schemes were introduced to address applications that require collective agreement by authorized personnel to access or modify, such as missile launching, sophisticated medical agreements, or high-level bank transfers. In standard secret sharing, the target key is distributed among several authorized participants in a way that only the intended group of them are needed to reconstruct the original target key. These schemes became popular as they fortified the security of both cryptography and steganography and accomplished extraordinary results combined with each. This work focuses on two particular secret sharing techniques known as counting-based secret sharing and matrix-based secret sharing, which is based on the former. These methods are simple and intuitive. Consequently, they are prone to attacks that may result in the successful guessing of the key. In this work, not only are the shares hidden but they are also encrypted beforehand so that should they be intercepted, the adversary cannot decipher them. In other words, two layers of security are added to the secret-sharing method: steganography and cryptography. We studied two image steganography methods: least significant bit (LSB) and discrete wavelet transform (DWT), each combined with XOR encryption for security and robustness acceptability verification. The research results demonstrated that the use of steganography and encryption along with the matrix-based secret sharing did not affect the quality of operation nor compromised the security of information presenting attractive remarks. © 2021 The Authors

Abstract With the rapid evolution of data over the last few years, many new technologies have arisen with artificial intelligent (AI) technologies at the top. Artificial intelligence (AI), with its infinite power, holds the potential to transform patient healthcare. Given the gaps revealed by the 2020 COVID-19 pandemic in healthcare systems, this research investigates the effects of using an artificial intelligence-driven public healthcare framework to enhance the decision-making process using an extended model of Shaft and Vessey (2006) cognitive fit model in healthcare organizations in Saudi Arabia. The model was validated based on empirical data collected using an online questionnaire distributed to healthcare organizations in Saudi Arabia. The main sample participants were healthcare CEOs, senior managers/managers, doctors, nurses, and other relevant healthcare practitioners under the MoH involved in the decision-making process relating to COVID-19. The measurement model was validated using SEM analyses. Empirical results largely supported the conceptual model proposed as all research hypotheses are significantly approved. This study makes several theoretical contributions. For example, it expands the theoretical horizon of Shaft and Vessey's (2006) CFT by considering new mechanisms, such as the inclusion of G2G Knowledge-based Exchange in addition to the moderation effect of Experience-based decision-making (EDBM) for enhancing the decision-making process related to the COVID-19 pandemic. More discussion regarding research limitations and future research directions are provided as well at the end of this study.

Abstract Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position’s reactivity and exhibits interesting behavior on neighboring bases in the sequence.

AbstractIdentifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

We examine how social contacts and feelings of solidarity shape experiences of loneliness during the COVID-19 lockdown in early 2020. From the PsyCorona database, we obtained longitudinal data from 23 countries, collected between March and May 2020. The results demonstrated that although online contacts help to reduce feelings of loneliness, people who feel more lonely are less likely to use that strategy. Solidarity played only a small role in shaping feelings of loneliness during lockdown. Thus, it seems we must look beyond the current focus on online contact and solidarity to help people address feelings of loneliness during lockdown. Finally, online contacts did not function as a substitute for face-to-face contacts outside the home—in fact, more frequent online contact in earlier weeks predicted more frequent face-to-face contacts in later weeks. As such, this work provides relevant insights into how individuals manage the impact of restrictions on their social lives.

Background The higher risk of COVID-19 in healthcare workers (HCWs) is well-known. However, the risk within HCWs is not fully understood. The objective was to compare the COVID-19 risk in intensive care unit (ICU) versus non-ICU locations. Methods A prospective surveillance study was conducted among HCWs at a large tertiary care facility in Riyadh between March 1st to November 30th, 2020. HCWs included both clinical (provide direct patient care) and non-clinical positions (do not provide direct patient care). Results A total 1,594 HCWs with COVID-19 were included; 103 (6.5%) working in ICU and 1491 (93.5%) working in non-ICU locations. Compared with non-ICU locations, ICU had more nurses (54.4% versus 22.1%, p<0.001) and less support staff (2.9% versus 53.1%, p<0.001). COVID-19 infection was similar in ICU and non-ICU locations (9.0% versus 9.8%, p=0.374). However, it was significantly higher in ICU nurses (12.3% versus 6.5%, p<0.001). Support staff had higher risk than other HCWs, irrespective of ICU working status (15.1% versus 7.2%, p<0.001). The crude relative risk (RR) of COVID-19 in ICU versus non-ICU locations was 0.92, 95% confidence interval [CI] was 0.76-1.11 (p=0.374). However, RR adjusted for professional category was significantly increased to 1.23, 95% CI 1.01-1.50 (p=0.036). Conclusion ICU had a significantly higher risk of COVID-19 infection only after adjusting for the distribution and risk of different professional categories. The later is probably determined by both exposure level and protection practices. The finding underscores the importance of strict implementation of preventive measures among all HCWs, including those performing non-clinical services.

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.

Abstract In the current manuscript, we deal with the dynamics of a piecewise covid-19 mathematical model with quarantine class and vaccination using SEIQR epidemic model. For this, we discussed the deterministic, stochastic, and fractional forms of the proposed model for different steps. It has a great impact on the infectious disease models and especially for covid-19 because in start the deterministic model played its role but with time due to uncertainty the stochastic model takes place and with long term expansion the use of fractional derivatives are required. The stability of the model is discussed regarding the reproductive number. Using the non-standard finite difference scheme for the numerical solution of the deterministic model and illustrate the obtained results graphically. Further, environmental noises are added to the model for the description of the stochastic model. Then take out the existence and uniqueness of positive solution with extinction for infection. Finally, we utilize a new technique of piecewise differential and integral operators for approximating Caputo-Fabrizio fractional derivative operator for the purpose of constructing of the fractional-order model. Then study the dynamics of the models such as positivity and boundedness of the solutions and local stability analysis. Solved numerically fractional-order model used Newton Polynomial scheme and present the results graphically.

Abstract The coronavirus infectious disease (COVID-19) is a novel respiratory disease reported in 2019 in China. The infection is very destructive to human lives and caused millions of deaths. Various approaches have been made recently to understand the complex dynamics of COVID-19. The mathematical modeling approach is one of the considerable tools to study the disease spreading pattern. In this article, we develop a fractional order epidemic model for COVID-19 in the sense of Caputo operator. The model is based on the effective contacts among the population and environmental impact to analyze the disease dynamics. The fractional models are comparatively better in understanding the disease outbreak and providing deeper insights into the infectious disease dynamics. We first consider the classical integer model studied in recent literature and then we generalize it by introducing the Caputo fractional derivative. Furthermore, we explore some fundamental mathematical analysis of the fractional model, including the basic reproductive number R 0 and equilibria stability utilizing the Routh-Hurwitz and the Lyapunov function approaches. Besides theoretical analysis, we also focused on the numerical solution. To simulate the model, we use the well-known generalized Adams–Bashforth Moulton Scheme. Finally, the influence of some of the model essential parameters on the dynamics of the disease is demonstrated graphically.