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The bioprinting technique with specialized tissue production allows the study of biological, physiological, and behavioral changes of cancerous and non-cancerous tissues in response to pharmacological compounds in personalized medicine. To this end, to evaluate the efficacy of anticancer drugs before entering the clinical setting, tissue engineered 3D scaffolds containing breast cancer and derived from the especially patient, similar to the original tissue architecture, can potentially be used. Despite recent advances in the manufacturing of 3D bioprinted breast cancer tissue (BCT), many studies still suffer from reproducibility primarily because of the uncertainty of the materials used in the scaffolds and lack of printing methods. In this review, we present an overview of the breast cancer environment to optimize personalized treatment by examining and identifying the physiological and biological factors that mimic BCT. We also surveyed the materials and techniques related to 3D bioprinting, i.e, 3D bioprinting systems, current strategies for fabrication of 3D bioprinting tissues, cell adhesion and migration in 3D bioprinted BCT, and 3D bioprinted breast cancer metastasis models. Finally, we emphasized on the prospective future applications of 3D bioprinted cancer models for rapid and accurate drug screening in breast cancer.

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 h of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.