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• W2507840163 endingPage "259" @default.
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• W2507840163 abstract "In general, sepsis is recognized as the “blood poisoning disease” because sepsis occurs when harmful chemical substances enter the blood. In clinics, sepsis is formally defined as a potentially life-threatening complication of diseases accompanied by symptoms such as high fever, hot flushed skin, elevated heart rate, altered mental status, and so on. If sepsis progresses to severe sepsis or septic shock, organ dysfunction occurs, which leads to a high chance of death. Patients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Therefore, it is necessary to find an accurate and effective tool that can help physicians predict the progression of disease in a patient-specific way to prevent possible severe sepsis or septic shock to lower risk for patients. This chapter presents a 14-equation system dynamics mathematical model ( SDMM ), which models and simulates the basic components of the innate immune response during acute inflammatory response (AIR), the initial stage of sepsis. Our goal is to formally model and provide insights into the dynamic effects of biomarkers in AIR , especially focusing on interactions between pro-inflammatory and anti-inflammatory cytokines during the development of disease. Our simulated results described dynamic patterns of AIR based on patients' initial immune conditions and revealed the important but underexplored dynamic behaviors of anti-inflammatory cytokines on sepsis progression. After the initial model calibration and validation, sensitivity analysis and stability analysis were carried out using bifurcation analysis to explore the system stability during episodes of sepsis progression under various initial and boundary conditions. We present a new SDMM in this chapter. The strength of this model is that it incorporates the interactions and interplays between pro-inflammatory and anti-inflammatory cytokines and the possible pathogenesis of AIR based on the host's physiological conditions." @default.
• W2507840163 created "2016-09-16" @default.
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• W2507840163 date "2016-08-12" @default.
• W2507840163 modified "2023-10-12" @default.
• W2507840163 title "Mathematical Modeling of Innate Immunity Responses of Sepsis: Modeling and Computational Studies" @default.
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• W2507840163 doi "https://doi.org/10.1002/9781118919408.ch8" @default.
• W2507840163 hasPublicationYear "2016" @default.
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