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3D-bioprinting is a promising technology for a tissue scaffold fabrication in the case of damaged tissue/organ replacement. Collagen is one of the most appropriate hydrogel for the purpose, due to its exceptional biocompatibility. However, the use of collagen with conventionally low concentration makes bioprinting process difficult and does not provide its high accuracy. The purpose of the study was evaluation of suitability of collagen with high concentration in case of chondrocyte-laden scaffold fabrication via 3D-bioprinting for cartilage regeneration in vitro and in vivo. The results of the study showed that inherent porosity of 4% collagen was not enough for cell survival in the case of long-term incubation in vitro. With the beginning of the scaffold incubation, cell migration to the surface and out of the scaffold was observed. The residual cells died mostly within 4 weeks. As for in vivo study, in 2 weeks after implantation of the scaffold, a weak granulomatous inflammation was observed. In 6 weeks, a connective tissue was formed in the area of implantation. In the tissue, macrophages and groups of small cells with round nuclei were found. In accordance with morphological criteria, these cells could be considered as young chondrocytes. However, its amount was not enough to initiate the formation of cartilage.

3D Bioprinting is a dynamically developing technology for tissue engineering and regenerative medicine. The main advantage of this technique is its ability to reproduce a given scaffold geometry and structure both in terms of the shape of the tissue-engineered construct and the distribution of its components. The key factor in bioprinting is bio ink, a cell-laden biocompatible material that mimics extracellular matrix. To meet all the requirements, the bio ink must include not only the main material, but also other components ensuring cell proliferation, differentiation and scaffold performance as a whole. The purpose of this review is to describe the most common materials applicable in bioprinting, consider their properties, prospects and limitations in cartilage restoration.

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Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients’ cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.

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