ABSTRACT
Facing the more contagious COVID-19 variant, Omicron, nonpharmaceutical interventions (NPIs) were still in place and booster doses were proposed to mitigate the epidemic. However, the uncertainty and stochasticity in individuals' behaviours toward the NPIs and booster dose increase, and how this randomness affects the transmission remains poorly understood. We present a model framework to incorporate demographic stochasticity and two kinds of environmental stochasticity (notably variations in adherence to NPIs and booster dose acceptance) to analyze the effects of different forms of stochasticity on transmission. The model is calibrated using the data from December 31, 2021, to March 8, 2022, on daily reported cases and hospitalizations, cumulative cases, deaths and vaccinations for booster doses in Toronto, Canada. An approximate Bayesian computational (ABC) method is used for calibration. We observe that demographic stochasticity could dramatically worsen the outbreak with more incidence compared with the results of the corresponding deterministic model. We found that large variations in adherence to NPIs increase infections. The randomness in booster dose acceptance will not affect the number of reported cases significantly and it is acceptable in the mitigation of COVID-19. The stochasticity in adherence to NPIs needs more attention compared to booster dose hesitancy. © 2023 American Institute of Mathematical Sciences. All rights reserved.
ABSTRACT
Introduction: COVID-19 has spread out all around the world and seriously interrupted human activities. Being a newfound disease, not only many aspects of the disease are unknown, but also there is not an effective medication to cure the disease. Besides, designing a drug is a time-consuming process and needs large investment. Hence, drug repurposing techniques, employed to discover the hidden benefits of the existing drugs, maybe a useful option for treating COVID-19. Methods: The present study exploits the drug repositioning concepts and introduces some candidate drugs which may be effective in controlling COVID-19. The suggested method consists of three main steps. First, the required data such as the amino acid sequences of targets and drug-target interactions are extracted from the public databases. Second, the similarity score between the targets (protein/enzymes) and genome of SARS-COV-2 is computed using the proposed fuzzy logic-based method. Since the classical approaches yield outcomes which may not be useful for the real-world applications, the fuzzy technique can address the issue. Third, after ranking targets based on the obtained scores, the usefulness of drugs affecting them is examined for managing COVID-19. Results: The results indicate that antiviral medicines, designed for curing hepatitis C, may also cure COVID-19. According to the findings, ribavirin, simeprevir, danoprevir, and XTL-6865 may be helpful in controlling the disease. Conclusion: It can be concluded that the similarity-based drug repurposing techniques may be the most suitable option for managing emerging diseases such as COVID-19 and can be applied to a wide range of data. Also, fuzzy logic-based scoring methods can produce outcomes which are more consistent with the real-world biological applications than others.
ABSTRACT
In this conceptual review, based on the protein-RNA recognition code, some theoretical sequences were detected in the spike (S), membrane (M) and capsid (N) proteins that may post-transcriptionally regulate the host genes/proteins in immune homeostasis, pulmonary epithelial tissue homeostasis, and lipid homeostasis. According to the review of literature, the spectrum of identified genes/proteins shows that the virus promotes IL1α/ß-IL1R1 signaling (type 1 immunity) and immunity defense against helminths and venoms (type 2 immunity). In the alteration of homeostasis in the pulmonary epithelial tissue, the virus blocks the function of cilia and the molecular programs that are involved in wound healing (EMT and MET). Additionally, the protein-RNA recognition method described here identifies compatible sequences in the S1A-domain for the post-transcriptional promotion of PIKFYVE, which is one of the critical factors for SARS-CoV-2 entry to the host cell, and for the post-transcriptional repression of xylulokinase XYLB. A decrease in XYLB product (Xu5P) in plasma was proposed as one of the potential metabolomics biomarkers of COVID-19. In summary, the protein-RNA recognition code leads to protein genes relevant to the SARS-CoV-2 life cycle and pathogenesis.
ABSTRACT
Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells).
ABSTRACT
The current effort is devoted to investigating and exploring the stochastic nonlinear mathematical pandemic model to describe the dynamics of the novel coronavirus. The model adopts the form of a nonlinear stochastic susceptible-infected-treated-recovered system, and we investigate the stochastic reproduction dynamics, both analytically and numerically. We applied different standard and nonstandard computational numerical methods for the solution of the stochastic system. The design of a nonstandard computation method for the stochastic system is innovative. Unfortunately, standard computation numerical methods are time-dependent and violate the structure properties of models, such as positivity, boundedness, and dynamical consistency of the stochastic system. To that end, convergence analysis of nonstandard computational methods and simulation with a comparison of standard computational methods are presented.