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Exudate absorption is a key parameter for proper wound dressing performance. Unlike standardized tests that consider exudate viscosity close to that of water, patients' exudates vary greatly in composition and, therefore, viscosity. This work aimed to investigate the effects of exudate viscosity and pore size of hydrogel-like dressings on the exudate absorption rate to establish rational criteria for the design of dressings that can meet the personalized needs of wound treatment. Computer-aided design (CAD) was used for Digital Light Processing (DLP) 3D printing of hydrogels with 0%, 30% and 60% porosity. The hydrogels were characterized in detail, and the absorption of two simulated exudate fluids (SEFs) was video-recorded. The same CAD files were used to develop in silico models to simulate exudate uptake rate. Both in vitro data and in silico modeling revealed that low-viscosity SEF penetrates faster through relatively small hydrogel pores (approx. 400 μm) compared to larger pores (approx. 1100 μm) due to capillary forces. However, in vitro vertical uptake took longer than when simulated using CAD design due to lateral fluid absorption through the pore walls in the hydrogel bulk. Distortions of hydrogel channels (micro-CT images) and lateral fluid absorption should be both considered for in silico simulation of SEF penetration. Overall, the results evidenced that porous hydrogel dressings allow rapid penetration (within a few seconds) and hosting of exudates, especially for pore size

Social networks constitute an almost endless source of social behavior information. In fact, sometimes the amount of information is so large that the task to extract meaningful information becomes impossible due to temporal constrictions. We developed an artificial-intelligence-based method that reduces the calculation time several orders of magnitude when conveniently trained. We exemplify the problem by extracting data freely available in a commonly used social network, Twitter, building up a complex network that describes the online activity patterns of society. These networks are composed of a huge number of nodes and an even larger number of connections, making extremely difficult to extract meaningful data that summarizes and/or describes behaviors. Each network is then rendered into an image and later analyzed using an AI method based on Convolutional Neural Networks to extract the structural information.