Matching Items (7)
Filtering by
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
Through a standpoint feminist perspective (Harding 2009) I conducted a situational analysis (Clarke, 2015) that examined academic literature and cancer support discussion boards (DBs) to identify how Western biomedicine, specifically oncology, can integrate complementary and alternative medicine (CAM) to improve cancer treatment in children. The aims of this project were: 1) to identify the CAM treatments that are being used to alleviate the side effects from oncological treatments and/or treat pediatric cancers; 2) to compare the subjective experience of CAM to Western biomedicine of cancer patients who leave comments on Group Loop, Cancer Compass and Cancer Forums, which are online support groups (N=20). I used grounded theory and situational mapping to analyze discussion threads. The participants identified using the following CAM treatments: herbs, imagery, prayer, stinging nettle, meditation, mind-body therapies and supplements. The participants turned to CAM treatments when their cancer was late-stage or terminal, often as an integrative and not exclusively to treat their cancer. CAM was more "effective" than biomedical oncology treatment at improving their overall quality of life and functionality. We found that youth on discussion boards did not discuss CAM treatments like the adult participants, but all participants visited these sites for support and verification of their cancer treatments. My main integration recommendation is to combine mind-body CAM therapies with biomedical treatment. This project fills the gap in literature that ignores the ideas of vulnerable populations by providing the experiences of adult and pediatric cancer patients, and that of their families. It is applicable to areas of the social studies of medicine, patient care, and families suffering from cancer. KEYWORDS: Cancer; Complementary and Alternative Medicine; Situational Analysis; Standpoint Feminism
ContributorsEsposito, Sydney Maria (Author) / Martinez, AirÃÂn (Thesis director) / Hruschka, Daniel (Committee member) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Cancer is the second leading cause of death in the United States. Cancer is a serious, complex disease which causes cells to grow uncontrollably, causing millions of deaths per year [1]. Cancer is usually caused by a combination of environmental variables and biological pathways. The pathways have a very robust structure normally, but are altered because of cancer, resulting in a loss of connectivity between pathways. In order detect these pathways, a PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) was created, which measures the relative rankings of the genes in each pathway. Applying this algorithm will allow us to figure out what pathways differed significantly in areas with cancer and areas without cancer. This would allow scientists to focus on specific pathways in order to learn more about the cancer and find more effective ways to treat it. So far, analysis using PoTRA has been successfully conducted on hepatocellular carcinoma (HCC) and its subtypes, resulting in all significant pathways found being cancer-associated. Now, using the TCGA data stored in Google Cloud's BigQuery, we created a pipeline to apply PoTRA to other cancer data sets and see how well it cross-applies to other cancers. The results show that even though some modification may need to be made to adapt to other datasets, many significant pathways were found for both HCC and breast cancer.
ContributorsMahesh, Sunny Nishant (Author) / Valentin, Dinu (Thesis director) / Liu, Li (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Chemotherapy refers to the use of chemical agents to inhibit or stop the growth of rapidly dividing cancer cells. There are many side effects of systemic chemotherapy, which are caused because the drug not only kills cancer cells but healthy cells as well (American Cancer Society, 2017). Common side effects include fatigue, hair loss, bruising/ bleeding, infection, anemia, nausea and vomiting, appetite changes, constipation, diarrhea, oral sores, nerve and muscle pain, dry skin and color change, kidney dysfunction, weight loss, cognitive difficulties, mood changes, sexual dysfunction, and fertility problems (American Cancer Society, 2017). Research shows that complementary and alternative medicine (CAM) may help relieve some of the side effects of chemotherapy. Examples of CAM include herbal medicine, dietary supplements, acupuncture, yoga, Tai Chi, massage, electromagnetic therapy, meditation, biofeedback, music, dance, and guided imagery (Johns Hopkins Medicine, 2017). The aim of this creative project was to design a third-party website to provide information to patients undergoing chemotherapy and their family members regarding the use of CAM for the treatment of chemotherapy-induced side effects. Rationale for this project stemmed from a preliminary research step. We analyzed and coded for presence or absence of CAM-specific information on the websites of 20 National Cancer Institute-designated comprehensive cancer centers across the United States. Fifty percent of websites were double-coded. Inter-rater reliabilities (kappa values) for coding of the presence or absence of specific CAM therapies ranged from 0.38 for acupuncture to 1.00 for exercise and yoga, expressive arts, and herbs (mean kappa = 0.75). Fourteen of the 20 websites mentioned meditation or mindfulness; 13 mentioned nutrition; 12 mentioned acupuncture; 11 mentioned exercise or yoga; 11 mentioned massage; 8 mentioned expressive arts; and 3 mentioned herbs. Frequencies for presence of either a description of the specific CAM therapy or an explanation of how the therapy works were lower. We then conducted a literature review using PUBMED to find peer-reviewed research on the efficacy of the previously described seven CAM therapies. The literature search focused on systematic reviews and meta-analyses published within the past 10 years. Based on the literature obtained, we created summaries of the scientific evidence for each CAM therapy. This information is now provided on our third-party website in tabular form with summative statements. The website describes in lay language: chemotherapy, chemotherapy side effects, CAM, seven specific CAM therapies, and evidence for the efficacy or lack thereof of each. Per the American Nurses Association (2015), it is our responsibility to advocate for our patients through education and holistic treatment. The role of the nurse is to educate the patient about treatment options; however, it is not within the nurse's scope of practice to prescribe a treatment. As such, this website should not be viewed as a prescription for CAM therapies, but instead as a user-friendly and easily accessible resource for informed decision-making regarding the adjunctive use of CAM therapies.
ContributorsNichols, Abrianna (Co-author) / Varosky, Maxwell (Co-author) / Langer, Shelby (Thesis director) / Morris, Brenda (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
ContributorsMillea, Timothy Michael (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Big Data Network Analysis of Genetic Variation and Gene Expression in Individuals with Breast Cancer
Description
The advent of big data analytics tools and frameworks has allowed for a plethora of new approaches to research and analysis, making data sets that were previously too large or complex more accessible and providing methods to collect, store, and investigate non-traditional data. These tools are starting to be applied in more creative ways, and are being used to improve upon traditional computation methods through distributed computing. Statistical analysis of expression quantitative trait loci (eQTL) data has classically been performed using the open source tool PLINK - which runs on high performance computing (HPC) systems. However, progress has been made in running the statistical analysis in the ecosystem of the big data framework Hadoop, resulting in decreased run time, reduced storage footprint, reduced job micromanagement and increased data accessibility. Now that the data can be more readily manipulated, analyzed and accessed, there are opportunities to use the modularity and power of Hadoop to further process the data. This project focuses on adding a component to the data pipeline that will perform graph analysis on the data. This will provide more insight into the relation between various genetic differences in individuals with breast cancer, and the resulting variation - if any - in gene expression. Further, the investigation will look to see if there is anything to be garnered from a perspective shift; applying tools used in classical networking contexts (such as the Internet) to genetically derived networks.
ContributorsRandall, Jacob Christopher (Author) / Buetow, Kenneth (Thesis director) / Meuth, Ryan (Committee member) / Almalih, Sara (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing (since many assays require a minimum tumor content to report variants at the limit of detection) may all be improved with more accurate and reproducible estimates of tumor content. Currently, tumor percentages of samples submitted for molecular testing are estimated by visual examination of Hematoxylin and Eosin (H&E) stained tissue slides under the microscope by pathologists. These estimations can be automated, expedited, and rendered more accurate by applying machine learning methods on digital whole slide images (WSI).
ContributorsCirelli, Claire (Author) / Yang, Yezhou (Thesis director) / Yalim, Jason (Committee member) / Velu, Priya (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05