Bio

Current Role at Stanford


I am the Product Manager for Stanford Profiles and Project Manager of the Stanford Profiles University-Wide Project. I work directly with the Stanford Profiles development team in IRT Application Services in the School of Medicine. On this project, I also work with a university-wide project team, the Stanford Profiles (formerly CAP) Working Group, made up of members from many of the Stanford schools and organizations. In June of 2014, we started the CAP Drupal Module Working Group. The purpose of this group is to review and improve the integration process with CAP by expanding and enhancing the CAP Drupal Module (CAPx) with an open-source contribution approach (an initiative started and led by SWS in collaboration with the CAP Drupal Module user community).

If you are interested in becoming part of the Stanford Profiles University-Wide Project, which includes the Stanford Profiles Public and Stanford-only View web sites or would like more information on integrating your web site with Stanford Profiles, contact me at tdelcont@stanford.edu.

Projects


  • Stanford Profiles (CAP) University Wide Project, Stanford University / SOM IRT (4/22/2013 - Present)

    Location

    Stanford, CA

    Collaborators

    • Dean and Members of , The Office of the Vice Provost and Dean of Research, Stanford University
    • Stanford Profiles (CAP) Working Group, Campus-Wide, Stanford University
    • Stanford Profiles Executive Oversight Committee, Representatives, Organizations across Stanford University
    • Michael Halaas, Chief Information Officer & Associate Dean, Industry Relations & Digital Heal, SoM - Information Resources & Technology, SoM - Information Resources & Technology
    • Don Mitchell, SoM - Information Resources & Technology
    • SOM IRT, Application Services, Development Team
    • Administrative Systems, Operations, Stanford University

Professional

Professional Affiliations and Activities


  • Member, Stanford Profiles Executive Oversight Group (2015 - Present)
  • Member and Certified Scrum Product Owner(R) Professional, Scrum Alliance (2015 - Present)
  • Group Facilitator, CAP Drupal Module Working Group (2014 - Present)
  • Group Leader, Stanford Profiles (CAP) Working Group (2013 - Present)

Publications

All Publications


  • Mass spectrometry-based proteomics techniques and their application in ovarian cancer research JOURNAL OF OVARIAN RESEARCH Swiatly, A., Plewa, S., Matysiak, J., Kokot, Z. J. 2018; 11: 88

    Abstract

    Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

    View details for DOI 10.1186/s13048-018-0460-6

    View details for Web of Science ID 000446509500001

    View details for PubMedID 30270814

    View details for PubMedCentralID PMC6166298

  • Abstracts: AACR Special Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Pittsburgh, PA, USA, October 1-4, 2017 Abstracts [Anonymous] AMER ASSOC CANCER RESEARCH. 2018: 17?48
  • Precious GEMMs: emergence of faithful models for ovarian cancer research JOURNAL OF PATHOLOGY Stuckelberger, S., Drapkin, R. 2018; 245 (2): 129?31

    Abstract

    The development of Genetically Engineered Mouse Models (GEMMs) has catalyzed tremendous progress in cancer research. However, it has been difficult to design adequate mouse models for high-grade serous carcinoma (HGSC), the most common and lethal form of ovarian cancer. The genetic complexity of the disease, as well as the recent appreciation that most HGSCs arise from the fallopian tube (FT) secretory epithelium rather than the ovarian surface epithelium, has stifled the development of robust GEMMs. In a recent issue of this journal, Zhai et al presented an elegant mouse model for ovarian cancer that uses Ovgp1 as an FT-specific promoter to inactivate Brca1, Trp53, Rb1, Nf1, and Pten. The authors showed that loss of these genes in the mouse FT epithelium can mimic the different stages of human HGSC tumorigenesis. Their robust model emphasizes the importance of considering both the cell of origin and tumor genetics in developing accurate model systems. They provide a useful tool for studying mechanisms of disease in vivo and for research into novel methods of prevention, early detection, and treatment of HGSC. Copyright 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

    View details for DOI 10.1002/path.5065

    View details for Web of Science ID 000432035600001

    View details for PubMedID 29493783

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