Optimized fast non-singular integral terminal sliding mode control of immune response and HCMV infection of renal transplant recipient

Kidneys are the most commonly transplanted organs, and renal transplant is the best treatment for patients with advanced stages of renal disease. Immunosuppressive drugs are used after renal transplant to prevent the body from rejecting the transplanted kidney and ensure its proper kidney functioning. However, suppression of the immune system increases the risk of viral infections and other complications. Therefore, careful monitoring and management of immunosuppressive and antiviral drugs are essential for the success of the transplants. This article presents a hybrid fast non-singular integral terminal sliding mode control technique to adjust the efficacies of these drugs in renal transplant recipients, ensuring successful transplants and preventing viral infections. The proposed strategy tracks system trajectories to reference values and adjusts the treatment plan accordingly. The Lyapunov stability theorem is used to prove the asymptotic stability of the closed-loop system. Several simulation studies are conducted in MATLAB/Simulink environment to evaluate the performance of the proposed control technique in maintaining a balance between over-suppression and under-suppression. Genetic Algorithm is used to optimize the gain values to further improve the performance of the proposed control technique. Its performance is compared with two other variants of terminal sliding mode controllers to demonstrate its effectiveness against them.

The possible role of Helicobacter pylori in liver diseases

According to previous studies, Helicobacter pylori infection is associated with liver disease. In order to better understand the risk of acquiring various liver diseases, we reviewed current knowledge on the impact of H. pylori on the onset, intensification, and progression of various liver diseases caused by the infection of H. pylori. It has been estimated that between 50 and 90% of people worldwide have been infected with H. pylori. The bacterium is mostly responsible for inflamed gastric mucosa, ulcers, and cancers associated with the gastric mucosa. Through the active antioxidant system in H. pylori, the bacteria can neutralize free radicals by synthesizing VacA, a toxin that causes cell damage and apoptosis. Furthermore, there is a possibility that CagA genes may play a role in cancer development. People who have been infected with H. pylori are likely to develop lesions in the skin, the circulation system, and the pancreas. Moreover, transferring blood from the stomach may allow H. pylori to colonize the liver. The bacterium worsened liver function during autoimmune inflammation, toxic injury, chronic HCV infection, chronic HBV infection, and liver cirrhosis. Increasing portal pressure, hyperammonemia, and esophageal varices may be associated with H pylori infection. As a result, it is crucial to diagnose and treat this infection in patients with H. pylori.

Empirical Modeling of COVID-19 Evolution with High/Direct Impact on Public Health and Risk Assessment

This report develops a conceivable mathematical model for the transmission and spread of COVID-19 in Romania. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures in the first wave of infection is crucial for assessing and evaluating the potential for sustained transmission occurring in the second wave. The main aim of the study was to emphasize the impact of control measures and the rate of case detection in slowing the spread of the disease. Non pharmaceutical control interventions include government actions, public reactions, and other measures. The methodology consists of an empirical model, taking into consideration the generic framework of the Stockholm Environment Institute (SEI) Epidemic–Macroeconomic Model, and incorporates the effect of interventions through a multivalued parameter, a stepwise constant varying during different phases of the interventions designed to capture their impact on the model. The model is mathematically consistent and presents various simulation results using best-estimated parameter values. The model can be easily updated later in response to real-world alterations, for example, the easing of restrictions. We hope that our simulation results may guide local authorities to make timely, correct decisions for public health and risk assessment.