In this work, a new model of breast cancer is proposed featuring both epithelial and stromal tissues arranged on a microfluidic chip. The main task of the work is the in vitro replication of the stromal activation during tumor epithelial invasion. The activation of tumor stroma and its morphological/compositional changes play a key role in tumor progression. Despite emerging evidences, to date the activation of tumor stroma in vitro has not been achieved yet. The tumor-on-chip proposed in this work is built in order to replicate the features of its native counterpart: multicellularity (tumor epithelial cell and stromal cell); 3D engineered stroma compartment composed of cell-assembled extracellular matrix (ECM); reliable 3D tumor architecture. During tumor epithelial invasion the stroma displayed an activation process at both cellular and ECM level. Similarly of what repeated in vivo, ECM remodeling is found in terms of hyaluronic acid and fibronectin overexpression in the stroma compartment. Furthermore, the cell-assembled ECM featuring the stromal tissue, allowed on-line monitoring of collagen remodeling during stroma activation process via real time multiphoton microscopy. Also, trafficking of macromolecules within the stromal compartment has been monitored in real time.
Today, it is increasingly recognized that air pollution hurts human health. Consequently, efficient mitigation strategies need to be implemented for substantial environmental and health co-benefits. A valid approach to reducing the air pollution effects on the environment and human health is proposed. Specific guidelines have been elucidated by differentiating them on the base of the final stakeholders (citizens, enterprises, and public authorities), of the emission sources (transport, household energy, industry, and energy generation sector, agriculture, and shipping area), and of the field of implementation (urban and extra-urban context). This paper can provide useful information for governments for the implementation of a strategic plan focused on emphasizing multi-pollutant emission reductions and overall air pollution-related risk.
Environmental pollution in urban areas may be mainly attributed to the rapid industrialization and increased growth of vehicular traffic. As a consequence of air quality deterioration, the health and welfare of human beings are compromised. Air quality monitoring networks usually are used not only to assess the pollutant trend but also in the effective set-up of preventive measures of atmospheric pollution. In this context, monitoring can be a valid action to evaluate different emission control scenarios; however, installing a high space-time resolution monitoring network is still expensive. Merge of observations data from low-cost air quality monitoring networks with forecasting models can contribute to improving significantly emission control scenarios. In this work, a validation algorithm of the forecasting model for the concentration of small particulates (PM10 and PM2.5) is proposed. Results showed a satisfactory agreement between the PM concentration forecast values and the measured data from 3 air quality monitoring stations. Final average RMSE values for all monitoring stations are equal to about 4.5 µg/m3.
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