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- Creators: Department of Psychology
- Creators: School of Mathematical and Statistical Sciences
- Status: Published
Past studies have shown that exercise in the form of high intensity interval training (HIIT) is the "ideal form of exercise to improve health and performance without overstressing the immune system" (Fisher et. al, 2011, p. 5). Additionally, HIIT has been found to promote cardiovascular health and immunity (Fisher et. al, 2011). The proposed study will evaluate the neuropsychological effects of HIIT on breast cancer patients undergoing anthracycline-based chemotherapy. The intervention group (n = 17) will receive a HIIT protocol concurrent with chemotherapy treatment. There will also be a control group (n= 17) to compare the effects of the intervention. Breast cancer survivorship is often ridden with various health and mental problems, the implementation of HIIT procedures could help to reduce these issues. It is expected that knowledge from this study will be useful in the healthcare setting to benefit breast cancer patients. This study will uniquely add to the limited research base by introducing an intervention for neuropsychological declines in breast cancer patients.
The synergistic effects between Vorinostat and Tamoxifen observed through a phase II study on breast cancer patients resistant to hormone therapy may involve more than the modulation of ER-alpha to reverse Tamoxifen resistance in ERBC cells. RT-qPCR of genes expressed in Tamoxifen resistant cells, trefoil factor 1(TFF1) and v-myc avian myelocytomatosis viral oncogene homolog (MYC), were evaluated along with ESR1 and Diablo as a control. MYC was observed to have increased expression in the treated cells, whereas the other genes had a decrease in their expression levels after the cells were treated for 3 days with Vorinostat IC30 of 1 µM. As for targeting the AR, MCF7 Tamoxifen sensitive and resistant cells were not affected by the AR antagonists to determine an IC50. The cell viability for all MCF7 sub-clones only decreased for high concentrations of 5.56 µM - 50 µM in Bicalutamide and 16.67 µM – 50 µM of MDV1300. Furthermore, hormone depletion of MCF7 G11 Tamoxifen resistant sub-clones did not show a great response to DHT stimulation or the AR antagonists. In the RT-qPCR, the MCF7 G11 cells showed an increase in mRNA expression for ER, AR, and PR after 4 hours of treatment with estradiol. As for the DHT treatment, ER, AR, PR, and PSA had a minimal increase in the fold change, but the fold change in AR was less than in the estradiol treatment. The Mayo Clinic will investigate the possible usage of AR as a biomarker through immunohistochemistry.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
Globally, breast cancer is the most common cancer in women. The disease and treatment cause many unfavorable symptoms such as fatigue, pain, and psychological stress. Research suggests that stress may negatively impact survival and recurrence outcomes of breast cancer patients. Cognitive-behavioral stress management (CBSM) programs have been found to improve these outcomes. In this thesis, I propose using virtual reality as a tool for CBSM to improve symptoms and outcomes for non-metastatic breast cancer patients.
Globally, breast cancer is the most common cancer in women. The disease and treatment cause many unfavorable symptoms such as fatigue, pain, and psychological stress. Research suggests that stress may negatively impact survival and recurrence outcomes of breast cancer patients. Cognitive-behavioral stress management (CBSM) programs have been found to improve these outcomes. In this thesis, I propose using virtual reality as a tool for CBSM to improve symptoms and outcomes for non-metastatic breast cancer patients.
