Using ex vivo culture to assess dynamic phenotype changes in human prostate macrophages following exposure to therapeutic drugs.

Within the prostate tumor microenvironment (TME) there are complex multi-faceted and dynamic communication occurring between cancer cells and immune cells. Macrophages are key cells which infiltrate and surround tumor cells and are recognized to significantly contribute to tumor resistance and metastases. Our understanding of their function in the TME is commonly based on in vitro and in vivo models, with limited research to confirm these model observations in human prostates. Macrophage infiltration was evaluated within the TME of human prostates after 72 h culture of fresh biopsies samples in the presence of control or enzalutamide. In addition to immunohistochemistry, an optimized protocol for multi-parametric evaluation of cellular surface markers was developed using flow cytometry. Flow cytometry parameters were compared to clinicopathological features. Immunohistochemistry staining for 19 patients with paired samples suggested enzalutamide increased the expression of CD163 relative to CD68 staining. Techniques to validate these results using flow cytometry of dissociated biopsies after 72 h of culture are described. In a second cohort of patients with Gleason grade group ≥ 3 prostate cancer, global macrophage expression of CD163 was unchanged with enzalutamide treatment. However, exploratory analyses of our results using multi-parametric flow cytometry for multiple immunosuppressive macrophage markers suggest subgroup changes as well as novel associations between circulating biomarkers like the neutrophil to lymphocyte ratio (NLR) and immune cell phenotype composition in the prostate TME. Further, we observed an association between B7-H3 expressing tumor-associated macrophages and the presence of intraductal carcinoma. The use of flow cytometry to evaluate ex vivo cultured prostate biopsies fills an important gap in our ability to understand the immune cell composition of the prostate TME. Our results highlight novel associations for further investigation.

Improving Energy Expenditure Estimation through Activity Classification and Walking Speed Estimation Using a Smartwatch.

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. The challenge is to provide accurate EE estimations in free-living environment through portable and unobtrusive devices. In this paper, we present an experimental study to estimate energy expenditure during sitting, standing and treadmill walking using a smartwatch. We introduce a novel methodology, which aims to improve the EE estimation by first separating sedentary (sitting and standing) and non-sedentary (walking) activities, followed by estimating the walking speeds and then calculating the energy expenditure using advanced machine learning based regression models. Ten young adults participated in the experimental trials. Our results showed that combining activity type and walking speed information with the acceleration counts substantially improved the accuracy of regression models for estimating EE. On average, the activity-based models provided 7% better EE estimation than the traditional acceleration-based models.