| LDR | | 03542nam u200493 4500 |
| 001 | | 000000422781 |
| 005 | | 20190215170414 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355628968 |
| 035 | |
▼a (MiAaPQ)AAI10641020 |
| 035 | |
▼a (MiAaPQ)umd:18566 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 519 |
| 100 | 1 |
▼a Botesteanu, Dana-Adriana.
▼0 (orcid)0000-0002-5509-8987. |
| 245 | 10 |
▼a Mathematical Models for Ovarian Cancer. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 175 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B. |
| 500 | |
▼a Adviser: Doron Levy. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2017. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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. |
| 520 | |
▼a 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. |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Applied mathematics. |
| 650 | 4 |
▼a Oncology. |
| 690 | |
▼a 0364 |
| 690 | |
▼a 0992 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Applied Mathematics and Scientific Computation. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-07B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996691
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |