Date of Award
5-2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
First Advisor
Ping Wang, MD
Second Advisor
Max Brenner, MD, PhD
Third Advisor
Theodoros P. Zanos, PhD
Abstract
This work consists of two independent segments, both of which attempt to extend the insights into pulmonary fibrosis and COVID-19 respiratory failure. After introducing the relevant state of knowledge (Chapter 1), in Chapters 2 and 3 we investigate the role of eCIRP in the fibrotic process in the lungs, which is a sequela of inflammatory lung diseases, and its effect on pulmonary fibroblasts. In Chapter 4 we go back to the beginning of the COVID-19 disease process and set out to predict respiratory failure in patients hospitalized with COVID-19, which is the most common cause of mortality in COVID-19 patients. Overall, we will show that extracellular cold inducible RNA binding protein (eCIRP) plays an important role in pulmonary fibrosis and activation of inflammatory fibroblasts. Furthermore, we show that machine learning can be used to predict early respiratory failure in COVID-19 patients. We hope that our work can be used to not only be used to develop therapeutics for patients at risk for developing pulmonary fibrosis, but also redesigned to develop prognostic models in current and future pandemics.
Recommended Citation
Bolourani, Siavash MD, MSc, "The Role of eCIRP in Pulmonary Fibrosis and Using Machine Learning to Predict Respiratory Failure" (2021). Elmezzi Graduate School of Molecular Medicine Theses. 1.
https://academicworks.medicine.hofstra.edu/elmezzi_theses/1