Background: Muscle wasting is a common phenomenon in oncology and appears to be alleviated by exercise training. This study aims to determine the degree of aggressiveness of cancer-induced muscle wasting in two different phenotypic muscles, and whether exercise training can attenuate this muscle dysfunction. Methods: Fifty Sprague-Dawley rats were randomly assigned to four experimental groups, two control groups (sedentary and exercised) and two breast cancer model groups (sedentary and exercised) induced by 1-methyl-1-nitrosoureia (MNU). After 35 weeks of endurance training the animals were sacrificed and the gastrocnemius and soleus muscles were harvested for morphometric analysis. Results: A significant reduction in cross-sectional area (P < 0.05) was found in both muscles of sedentary tumor-bearing animals. Interstitial fibrosis was significantly higher in the gastrocnemius of sedentary tumor-bearing animals (P < 0.05), but not in the soleus. A shift from large to small fibers was observed in the gastrocnemius of sedentary tumor-bearing animals. Long-term exercise training was able to prevent this cancer-related mus-cle dysfunction. Conclusions: The Gastrocnemius muscle showed a very pronounced reduction in cross-sectional area and marked interstitial fibrosis in sedentary animals with tumors. The soleus muscle showed a less pronounced but significant reduction in cross-sectional area, and collagen deposition did not differ between tumor groups. These contrasting results confirm that can-cer-induced muscle wasting can affect specific fiber types, and specific muscles, namely fast glycolytic muscles, and that exercise training can act to improve it.

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the circular Hough transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills need to be further improved, in particular for the detection of pills with non-oval shapes.