Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.

Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into how evolutionary history has shaped mechanisms of cancer suppression by examining how life history traits impact cancer susceptibility across species. Here, we perform multi-level analysis to test how species-level life history strategies are associated with differences in neoplasia prevalence, and apply this to mammary neoplasia within mammals. We propose that the same patterns of cancer prevalence that have been reported across species will be maintained at the tissue-specific level. We used a combination of factor analysis and phylogenetic regression on 13 life history traits across 90 mammalian species to determine the correlation between a life history trait and how it relates to mammary neoplasia prevalence. The factor analysis presented ways to calculate quantifiable underlying factors that contribute to covariance of entangled life history variables. A greater risk of mammary neoplasia was found to be correlated most significantly with shorter gestation length. With this analysis, a framework is provided for how different life history modalities can influence cancer vulnerability. Additionally, statistical methods developed for this project present a framework for future comparative oncology studies and have the potential for many diverse applications.

Advancing the understanding and treatment of many neurological disorders can be achieved by improving methods of neuronal detection at increased depth in the mammalian brain. Different cell subtypes cannot be detected using non-invasive techniques beyond 1 mm from cortical surface, in the context of targeting particular cell types in vivo (Wang, 2012). These limitations in the depth of imaging and targeting are due to optical scattering (Ntziachristos, 2010). In order to overcome these restrictions, longer wavelength fluorescent proteins have been utilized by researchers to see tagged cells at depth. Optical techniques such as two-photon and confocal microscopy have been used in combination with fluorescent proteins to expand depth, but are still limited by the penetration depth of light due to optical scattering (Lee, 2015). This research aims to build on other detection methods, such as the photoacoustic effect and automated fluorescence-guided electrophysiology, to overcome this limitation.

Cancer perception is different across each region of the world, which can lead to late diagnosis and death, especially in cancers associated with greater stigma. These perceptions are largely affected by sociocultural, economic, and healthcare infrastructure of the region. It is crucial that effective methods are implemented, so that patients do not avoid getting diagnosed and treated