Browsing by Author "Cristini, Vittorio"

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    Mechanistic modeling of pathological biomarkers to study Alzheimer’s disease progression
    (2021-04-19) Peláez Soní, María José; Igoshin, Oleg; Cristini, Vittorio
    Alzheimer’s disease (AD) is one of the leading causes of death in the United States. It is a neurodegenerative disorder that affects cognitive abilities, characterized by deterioration of the brain tissue due to synaptic loss caused by the abnormal accumulation of amyloid-β (Aβ) peptide and hyperphosphorylated tau proteins, leading to the formation of senile plaques and neurofibrillary tangles, respectively. Mathematical models based on AD biomarkers can be used as a tool to estimate disease progression kinetics, make disease prognosis, determine novel treatment strategies, and develop patient-specific treatment regimens. In this thesis, I developed a novel kinetic model, formulated as a system of differential equations, and a dynamic network diffusion model to characterize the spatiotemporal evolution of pathological biomarkers during AD progression. The kinetic model was calibrated with the ADNI database and simulated the temporal evolution of the five variables of the model: CSF tau, CSF phosphorylated tau, neuronal activity, CSF soluble Aβ, and Aβ plaques. Additionally, parametric analysis of the model highlighted key parameters responsible for disease progression, which hold the potential to design new treatment strategies.
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    Modeling and quantitative analysis to understand evolution, prognosis, and drug delivery in complex diseases
    (2023-11-27) Peláez Soní, María José; Cristini, Vittorio; Kono, Junichiro
    Modeling and quantitative analysis stand as indispensable tools in medicine, offering invaluable insights, predictive capabilities, and actionable solutions to address a myriad of healthcare challenges. Their instrumental role in advancing our understanding of disease dynamics has paved the way for enhanced diagnosis, prognosis, and treatment capabilities. This dissertation showcases the pivotal role of modeling methods and quantitative analysis in medicine, presented through three distinct applications that span over advanced drug delivery systems, cancer prognosis and the evolution of chemoresistance. In the first part of this work, a comprehensive mathematical model of transdermal drug delivery via microneedle-based patches, integrated with a pharmacokinetics model, is introduced. Model-based simulations were conducted to pinpoint the key parameters governing systemic delivery, enabling the optimization of patch designs to improve drug pharmacokinetics. In the second part, survival analysis is employed to identify biomechanical and immune biomarkers, enabling the prospective prediction of tumor aggressiveness, invasiveness, treatment outcomes, and survival probability in breast cancer. Lastly, a novel hypothesis is presented, proposing that water exclusion zones within cells may act as insulation barriers, safeguarding the delicate quantum nature of specific biochemical reactions against environmental influences. This hypothesis gains additional support through a review regarding the role that interfacial water plays in several biological processes and a proof of concept example to illustrate the application of quantum mechanics models for understanding the evolution of chemoresistance. Through these multifaceted investigations, this dissertation underscores the vital role that modeling and quantitative analysis plays in investigating the complexities of diseases, promising new horizons in medicine.