Omicron BA.2.75 Subvariant of SARS-CoV-2 Is Expected to Have the Greatest Infectivity Compared with the Competing BA.2 and BA.5, Due to Most Negative Gibbs Energy of Binding.

Omicron BA.2.75 may become the next globally dominant strain of COVID-19 in 2022. The BA.2.75 sub-variant has acquired more mutations (9) in spike protein and other genes of SARS-CoV-2 than any other variant. Thus, its chemical composition and thermodynamic properties have changed compared with earlier variants. In this paper, the Gibbs energy of the binding and antigen-receptor binding rate was reported for the BA.2.75 variant. Gibbs energy of the binding of the Omicron BA.2.75 variant is more negative than that of the competing variants BA.2 and BA.5.

Analogy between Consecutive Reaction Kinetics and the Spread of COVID-19 as a Student-Centered Learning Approach

The reaction rate and rate law are chemical kinetics concepts that undergraduate students have difficulty understanding and applying in real life. A further challenge is the overall reaction rate of consecutive reactions. Herein we present a creative teaching practice using the analogy-based approach to exploit the similarities between the chemical kinetics of consecutive reactions involved in ethanol oxidation and the model employed to describe the COVID-19 outbreak. Students conducted the mathematical modeling using open online software. Fitting the epidemic data from four different countries during the first wave of the COVID-19 pandemic to the SIR model and comparing the results with the model of ethanol oxidation brought students insight into the effects of kinetic parameters and triggered a discussion on conceptual kinetic fundamentals. This teaching approach sets up an environment where students can build knowledge that accounts for their pandemic experience, fostering mathematical and computational skills along with data analysis and interpretation that promotes a deeper understanding of the phenomena implicated in the kinetics of consecutive reactions. Mathematical modeling activities are here to stay and will continue gaining relevance in undergraduate kinetics courses, even without the lockdown, therefore the development of these kinds of learning strategies is of high significance worldwide.

Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions

Modeling complex chemical reaction networks has inspired a considerable body of research, and a variety of approaches to modeling nonlinear pathways are being developed. Here, a general methodology is formulated to convert an arbitrary reaction network into its equivalent electrical analog. The topological equivalence of the electrical analog is mathematically established for unimolecular reactions using Kirchoff's laws. The modular approach is generalized to bimolecular and nonlinear autocatalytic reactions. It is then applied to simulate the dynamics of nonlinear autocatalytic networks without making simplifying assumptions, such as use of the quasi-steady state/Bodenstein approximation and the assumption of an absence of nonlinear steps in the intermediates. This is among the few papers that quantify the dynamics of a nonlinear chemical reaction network by generating and simulating an electrical network analog. As a realistic biological application, the early phase of the spread of COVID-19 is modeled as an autocatalytic process, and the predicted dynamics are in good agreement with experimental data. The rate-limiting step of viral transmission is identified, leading to novel mechanistic insights.

Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection.

It is highly desired to explore the interventions of COVID-19 for early treatment strategies. Such interventions are still under consideration. A model is benchmarked research and comprises target cells, virus infected cells, immune cells, pro-inflammatory cytokines, and, anti-inflammatory cytokine. The interaction of the drug with the inflammatory sub-system is analyzed with the aid of kinetic modeling. The impact of drug therapy on the immune cells is modelled and the computational framework is verified with the aid of numerical simulations. The work includes a significant hypothesis that quantifies the complex dynamics of the infection, by relating it to the effect of the inflammatory syndrome generated by IL-6. In this paper we use the cancer immunoediting process: a dynamic process initiated by cancer cells in response to immune surveillance of the immune system that it can be conceptualized by an alternating movement that balances immune protection with immune evasion. The mechanisms of resistance to immunotherapy seem to broadly overlap with those used by cancers as they undergo immunoediting to evade detection by the immune system. In this process the immune system can both constrain and promote tumour development, which proceeds through three phases termed: (i) Elimination, (ii) Equilibrium, and, (iii) Escape [1]. We can also apply these concepts to viral infection, which, although it is not exactly "immunoediting", has many points in common and helps to understand how it expands into an "untreated" host and can help in understanding the SARS-CoV2 virus infection and treatment model.