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Cardiovascular diseases are responsible for a large number of severe disability cases and deaths worldwide. Strong research in this field has been extensively carried out, in particular for the associated complications, such as the occlusion of small-diameter (<6 mm) vessels. Accordingly, in the present research, two random copolyesters of poly(butylene 2,5-furandicarboxylate) (PBF) and poly(butylene isophthalate) (PBI), were successfully synthesized via two-step melt polycondensation and were thoroughly characterized from molecular, thermal, and mechanical perspectives. The copolymeric films displayed a peculiar thermal behavior, being easily processable in the form of films, although amorphous, with Tg close to room temperature. Their thermal stability was high in all cases, and from the mechanical point of view, the materials exhibited a high ultimate strength, together with values of elastic moduli tunable with the chemical composition. The long-term stability of these materials under physiological conditions was also demonstrated. Cytotoxicity was assessed using a direct contact assay with human umbilical vein endothelial cells (HUVECs). In addition, hemocompatibility was tested by evaluating the adhesion of blood components (such as the adsorption of human platelets and fibrinogen). As a result, a proper chemical design and, in turn, both the solid-state and functional properties, are pivotal in regulating cell behavior and opening new frontiers in the tissue engineering of soft tissues, including vascular tissues.

In recent years there has been a growing interest in the use of proteins as biocompatible and environmentally friendly biomolecules for the design of wound healing and drug delivery systems. Keratin is a fascinating protein, obtainable from several keratinous biomasses such as wool, hair or nails, with intrinsic bioactive properties including stimulatory effects on wound repair and excellent carrier capability. In this work keratin/poly(butylene succinate) blend solutions with functional properties tunable by manipulating the polymer blending ratios were prepared by using 1,1,1,3,3,3-hexafluoroisopropanol as common solvent. Afterwards, these solutions doped with rhodamine B (RhB), were electrospun into blend mats and the drug release mechanism and kinetics as a function of blend composition was studied, in order to understand the potential of such membranes as drug delivery systems. The electrophoresis analysis carried out on keratin revealed that the solvent used does not degrade the protein. Moreover, all the blend solutions showed a non-Newtonian behavior, among which the Keratin/PBS 70/30 and 30/70 ones showed an amplified orientation ability of the polymer chains when subjected to a shear stress. Therefore, the resulting nanofibers showed thinner mean diameters and narrower diameter distributions compared to the Keratin/PBS 50/50 blend solution. The thermal stability and the mechanical properties of the blend electrospun mats improved by increasing the PBS content. Finally, the RhB release rate increased by increasing the keratin content of the mats and the drug diffused as drug-protein complex.

The fast and continuous growth in number and quality of deepfake videos calls for the development of reliable detection systems capable of automatically warning users on social media and on the Internet about the potential untruthfulness of such contents. While algorithms, software, and smartphone apps are getting better every day in generating manipulated videos and swapping faces, the accuracy of automated systems for face forgery detection in videos is still quite limited and generally biased toward the dataset used to design and train a specific detection system. In this paper we analyze how different training strategies and data augmentation techniques affect CNN-based deepfake detectors when training and testing on the same dataset or across different datasets.

In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.