Antiretroviral therapy of HIV infection using a novel optimal type-2 fuzzy control strategy

Abstract The human immunodeficiency virus (HIV), as one of the most hazardous viruses, causes destructive effects on the human bodies' immune system. Hence, an immense body of research has focused on developing antiretroviral therapies for HIV infection. In the current study, we propose a new control technique for a fractional-order HIV infection model. Firstly, a fractional model of the HIV model is investigated, and the importance of the fractional-order derivative in the modeling of the system is shown. Afterward, a type-2 fuzzy logic controller is proposed for antiretroviral therapy of HIV infection. The developed control scheme consists of two individual controllers and an aggregator. The optimal aggregator modifies the output of each individual controller. Simulations for two different strategies are conducted. In the first strategy, only reverse transcriptase inhibitor (RTI) is used, and the superiority of the proposed controller over a conventional fuzzy controller is demonstrated. Lastly, in the second strategy, both RTI and protease inhibitors (PI) are used simultaneously. In this case, an optimal type-2 fuzzy aggregator is also proposed to modify the output of the individual controllers based on optimal rules. Simulations results demonstrate the appropriate performance of the designed control scheme for the uncertain system

Highly Mutable Linker Regions Regulate HIV-1 Rev Function and Stability.

HIV-1 Rev is an essential viral regulatory protein that facilitates the nuclear export of intron-containing viral mRNAs. It is organized into structured, functionally well-characterized motifs joined by less understood linker regions. Our recent competitive deep mutational scanning study confirmed many known constraints in Rev's established motifs, but also identified positions of mutational plasticity, most notably in surrounding linker regions. Here, we probe the mutational limits of these linkers by testing the activities of multiple truncation and mass substitution mutations. We find that these regions possess previously unknown structural, functional or regulatory roles, not apparent from systematic point mutational approaches. Specifically, the N- and C-termini of Rev contribute to protein stability; mutations in a turn that connects the two main helices of Rev have different effects in different contexts; and a linker region which connects the second helix of Rev to its nuclear export sequence has structural requirements for function. Thus, Rev function extends beyond its characterized motifs, and is tuned by determinants within seemingly plastic portions of its sequence. Additionally, Rev's ability to tolerate many of these massive truncations and substitutions illustrates the overall mutational and functional robustness inherent in this viral protein

Nonlinear Systems

Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems

Exploring the integration of traditional and molecular epidemiological methods for infectious disease outbreaks

BACKGROUND: Understanding the transmission dynamics of infectious pathogens is critical to developing effective public health strategies. Traditionally, time consuming epidemiological methods were used, often limited by incomplete or inaccurate datasets. Novel phylogenetic techniques can determine transmission events, but have rarely been used in real-time outbreak settings to inform interventions and limit the impact of outbreaks. METHODS: I undertook a series of novel studies to explore the utility of combining phylogenetics with traditional epidemiological analysis to enhance the understanding of transmission dynamics. I investigated HIV in an endemic South African setting and Ebola in an acute outbreak in Sierra Leone. The strengths and limitations of this combined approach are explored, ethical issues investigated and recommendations made regarding the implications of this work for public health. RESULTS: Phylogenetics provides an exciting and synergistic tool to epidemiological analysis in outbreak investigation and control. These combined methods enable a more detailed understanding than is possible through either discipline alone. My key findings include: • Identification of infection source: Phylogenetics gives new insight into the role of external introductions (e.g. migrators) in driving and sustaining the high incidence of HIV. • Earlier identification of new emerging clusters: I identified a new cluster of HIV from around a mining community. This is one of the first examples of molecular methods detecting a previously unknown outbreak. • Identification of novel mechanisms of transmission: This work suggests that children may have been infected by playing in puddles contaminated with Ebola, a previously unrecognised route of transmission. CONCLUSION: The integration of these two methods facilitate sophisticated real-time techniques to maximise understanding of transmission dynamics, allowing faster and more effectively targeted interventions. Moving forwards, sequence data should be incorporated into standard outbreak investigation. This is critical at a time when infectious disease outbreaks have led to the some of the most significant global health threats of the recent past

An Extended Fuzzy Discrete Event System For Hiv/aids Treatment Regimen Selection

HIV/AIDS is a global problem. Its treatment is dependent on the physician experts\u27 opinion. A system which is capable of supporting the treatment decision will be desired. Recently, the HIV/AIDS treatment regimen selection system appeared in literature that utilized theory of fuzzy discrete event system (FDES) to capture the meaning of experts\u27 knowledge; a form of consensus involving estimated points and type-1 fuzzy sets. The goal was to assign exact matching regimens as close as possible to those regimens preferred by the experts for patients. The system performance was 80% of satisfaction level with the 35 retrospective patients. Extracting experts\u27 knowledge into the consensus forms would not be possible without being compromised by the experts. With equal respective experts, if one insists on his/her values, then the consensus would not be achieved. Conversely, the FDES theory would be no longer to handle such conflict. The theory of extended fuzzy discrete event system (EFDES) extended the FDES theory that type-2 fuzzy sets would be allowed to be used in the system. This dissertation is to apply the EFDES theory to the HIV/AIDS treatment regimen selection system. Seven scenarios of the diversity of experts\u27 knowledge representation were categorized for the system. The MATLAB was implemented to model the system. Genetic algorithm in MATLAB\u27s Direct Search Toolbox was used to search an optimal vector of 26 weights for system parameters regarding the experts\u27 regimen-choices. As the same input of the retrospective patient data for the FDES-based system, the overall means of simulation results of EFDES-based system demonstrated the degree of matching regimens being 80%. That result would be the same performance level of the FDES-based system as well. The EFDES-based system performance with self-learning provided the overall satisfaction level of above 80%. Moreover, the EFDES-based system with use of the type-2 fuzzy set gained the benefit on the extraction of diverse and uncertainty experts\u27 knowledge and expertise