Search

Background: In all chronic airway diseases, the dynamics of airway function are influenced by underlying airway inflammation and bronchial hyperresponsiveness along with limitations in reversibility owing to airway and lung remodeling as well as mucous plugging. The relative contribution of each component translates into specific clinical patterns of symptoms, quality of life, exacerbation risk, and treatment success. Objective: We aimed to evaluate whether subgrouping of patients with obstructive airway diseases according to patterns of fluctuation in lung function allows identification of specific phenotypes with distinct clinical characteristics. Methods: We applied the novel method of fluctuation-based clustering (FBC) to twice-daily FEV1 measurements recorded over a 1-year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or chronic obstructive pulmonary disease from the European BIOAIR cohort. Results: Independently of clinical diagnosis, FBC divided patients into 4 fluctuation-based clusters with progressively increasing alterations in lung function that corresponded to patterns of increasing clinical severity, risk of exacerbation, and lower quality of life. Clusters of patients with airway disease with significantly elevated levels of biomarkers relating to remodeling (osteonectin) and cellular senescence (plasminogen activator inhibitor-1), accompanied by a loss of airway reversibility, pulmonary hyperinflation, and loss of diffusion capacity, were identified. The 4 clusters generated were stable over time and revealed no differences in levels of markers of type 2 inflammation (blood eosinophils and periostin). Conclusion: FBC-based phenotyping provides another level of information that is complementary to clinical diagnosis and unrelated to eosinophilic inflammation, which could identify patients who may benefit from specific treatment strategies or closer monitoring. © 2021

In this dissertation we study time discrete monomial dynamical systems as well as control systems over a finite field. Furthermore, we study the performance of a recently developed “reverse engineering” method that was conceived to reconstruct a time discrete dynamical system over a finite field out of biological time series data. We establish to what extent this method can be used to approximate the stochastic agent-based model PathSim, which simulates Epstein-Barr virus infection. The main results of this work are: Theorems that forecast the long term dynamics of the systems mentioned above and their algorithmic applications. Controllability results as well as methods for the synthesis of state feedback laws. Statements about the relationship between the performance of the “reverse engineering” method and the quantity or quality of the time series data used.