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Transcriptional analysis and live-cell imaging are a powerful tool to investigate the dynamics of complex biological systems. In vitro expanded porcine oral mucosal cells, consisting of populations of epithelial and connective lineages, are interesting and complex systems for study via microarray transcriptomic assays to analyze gene expression profile. The transcriptomic analysis included 56 ontological groups with particular focus on 7 gene ontology groups that are related to the processes of differentiation and development. Most analyzed genes were upregulated after 7 days and downregulated after 15 and 30 days of in vitro culture. The performed transcriptomic analysis was then extended to include automated analysis of differential interference contrast microscopy (DIC) images obtained during in vitro culture. The analysis of DIC imaging allowed to identify the different populations of keratinocytes and fibroblasts during seven days of in vitro culture, and it was possible to evaluate the proportion of these two populations of cells. Porcine mucosa may be a suitable model for reference research on human tissues. In addition, it can provide a reference point for research on the use of cells, scaffolds, or tissues derived from transgenic animals for applications in human tissues reconstruction.

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

Considering the potential of nanostructured titanium dioxide layers as drug delivery systems, it is advisable to indicate the possibility of creating a functional drug delivery system based on anodic TiO2 for celecoxib as an alternative anti-inflammatory drug and its inclusion complex with β-cyclodextrin. First, the optimal composition of celecoxib—β-cyclodextrin complexes was synthesized and determined. The effectiveness of the complexation was quantified using isothermal titration calorimetry (ITC), differential scanning calorimetry (DSC), infrared spectroscopy (FT-IR) nuclear magnetic resonance (1H NMR), and scanning electron microscopy (SEM). Then, nanostructured titanium dioxide layers (TiO2) were synthesized using the electrochemical oxidation technique. The TiO2 layers with pore diameters of 60 nm and layer thickness of 1.60 µm were used as drug delivery systems. The samples were modified with pure celecoxib and the β-cyclodextrin-celecoxib complex. The release profiles shown effective drug release from such layers during 24 h. After the initial burst release, the drug was continuously released from the pores. The presented results confirm that the use of nanostructured TiO2 as a drug delivery system can be effectively used in more complicated systems composed of β-cyclodextrin—celecoxib complexes, making such drugs available for pain treatment, e.g., for orthopedic surgeries.