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Mathematical Models for Ovarian Cancer

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자료유형	E-Book
개인저자	Botesteanu, Dana-Adriana.
단체저자명	University of Maryland, College Park. Applied Mathematics and Scientific Computation.
서명/저자사항	Mathematical Models for Ovarian Cancer.
발행사항	[S.l.] : University of Maryland, College Park., 2017
발행사항	Ann Arbor : ProQuest Dissertations & Theses, 2017
형태사항	175 p.
소장본 주기	School code: 0117.
ISBN	9780355628968
일반주기	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B. Adviser: Doron Levy.
이용제한사항	This item is not available from ProQuest Dissertations & Theses.
요약	Ovarian cancer is the most fatal cancer of the female reproductive system. High-grade serous ovarian cancer (HGSOC) represent the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. From a clinical pe
요약	Studying the growth, progression, and dynamic response to treatment of ovarian cancers in an integrated systems biology/mathematical framework offers an innovative tool at the disposal of the oncological community to further exploit readily avai
요약	As a first step, we developed a mathematical model for a quantitative explanation why transvaginal ultrasound-based (TVU) screening fails to improve low-volume detectability and overall survival (OS) of HGSOC. This mathematical model can accurat
요약	At the cell population level, we have quantitatively investigated the role of cell heterogeneity emerging from variations in cell-cycle parameters and cell-death. Many commonly used chemotherapeutic agents in treating ovarian cancers target only
요약	At the single cell level, we developed a mathematical model to explain the emerging heterogeneity in individual cancer cell responses to drugs targeting the cell-cycle, which have a broad spectrum of anti-tumor activity in ovarian cancers. This
요약	The model incorporates an intrinsic form of heterogeneity via the duration of time single cells spend in mitosis. It uses published single cell in vitro experimental data for calibration. Herein, the goal is to better understand why, within a d
요약	Studying the natural history, growth, and progression of ovarian cancers in an integrated systems biology/mathematical framework represents a complementary tool that can be used to provide valuable insights into the treatment of HGSOC.
요약	My work focuses on developing and applying quantitative, integrated mathematical modeling frameworks to pre-clinical and clinical data, in order to better understand ovarian cancer dynamics and develop new therapeutics.
일반주제명	Applied mathematics. Oncology.
언어	영어
기본자료 저록	Dissertation Abstracts International79-07B(E). Dissertation Abstract International
대출바로가기	http://www.riss.kr/pdu/ddodLink.do?id=T14996691