Tissue regeneration over a large defect with a subsequent satisfactory functional recovery still stands as a major problem in areas such as nerve regeneration or bone healing. The routine technique for the reconstruction of a nerve gap is the use of autologous nerve grafting, but still with severe complications. Over the last decades several attempts have been made to overcome this problem by using biomaterials as scaffolds for guided tissue regeneration. Despite the wide range of biomaterials available, functional recovery after a serious nerve injury is still far from acceptable. Prior to the use of a new biomaterial on healing tissues, an evaluation of the host's inflammatory response is mandatory. In this study, three chitosan membranes were tested in vitro and in vivo for later use as nerve guides for the reconstruction of peripheral nerves submitted to axonotmesis or neurotmesis lesions. Chitosan membranes, with different compositions, were tested in vitro, with a nerve growth factor cellular producing system, N1E-115 cell line, cultured over each of the three membranes and differentiated for 48h in the presence of 1.5% of DMSO. The intracellular calcium concentrations of the non-differentiated and of the 48h-differentiated cells cultured on the three types of the chitosan membranes were measured to determine the cell culture viability. In vivo, the chitosan membranes were implanted subcutaneously in a rat model, and histological evaluations were performed from material retrieved on weeks 1, 2, 4 and 8 after implantation. The three types of chitosan membranes were a viable substrate for the N1E-115 cell multiplication, survival and differentiation. Furthermore, the in vivo studies suggested that these chitosan membranes are promising candidates as a supporting material for tissue engineering applications on the peripheral nerve, possibly owing to their porous structure, their chemical modifications and high affinity to cellular systems.

Dairy cattle are particularly sensitive to heat stress due to the higher metabolic rate needed for milk production. In recent decades, global warming and the increase in dairy production in warmer countries have stimulated the development of a wide range of environmental control systems for dairy farms. Despite their proven effectiveness, the associated energy and water consumption can compromise the viability of dairy farms in many regions, due to the cost and scarcity of these resources. To make these systems more efficient, they should be activated in time to prevent thermal stress and switched off when that risk no longer exists, which must consider environmental variables as well as the variables of the animals themselves. Nowadays, there is a wide range of sensors and equipment that support farm routine procedures, and it is possible to measure several variables that, with the aid of algorithms based on predictive models, would allow anticipating animals’ thermal states. This review summarizes three types of approaches as predictive models: bioclimatic indexes, machine learning, and mechanistic models. It also focuses on the application of the current knowledge as algorithms to be used in the management of diverse types of environmental control systems.