Among many bioinks used for extrusion 3D bioprinting, the most commonly used bioink is the polysaccharide alginate because of its various cellular-friendly property like gelation. Erratic degradation and cell-binding motifs are not present in alginate which are the limitations of alginate bioinks, which can be improved by blending various low concentrations of natural or artificial polymers. Here in this chapter, we will discuss the various important properties of the alginate which make it as the bioink for almost all bioprinting scaffold designs as well as how improve the cellular properties like its cell-material interaction by blending it with other polymer solutions.
Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. In this chapter, we will discuss the application of artificial intelligence (AI) in modern healthcare system and the challenges of this system in detail. Different types of artificial intelligence devices are described in this chapter with the help of working mechanism discussion. Alginate, a naturally available polymer found in the cell wall of the brown algae, is used in tissue engineering because of its biocompatibility, low cost, and easy gelation. It is composed of α-L-guluronic and β-D-manuronic acid. To improve the cell-material interaction and erratic degradation, alginate is blended with other polymers. Here, we discuss the relationship of artificial intelligence with alginate in tissue engineering fields.
Fabrication of hollow channels with user-defined dimensions and patterns inside viscoelastic, gel-type materials is required for several applications, especially in biomedical engineering domain. These include objectives of obtaining vascularized tissues and enclosed or subsurface microfluidic devices. However, presently there is no suitable manufacturing technology that can create such channels and networks in a gel structure. The advent of three-dimensional bioprinting has opened new possibilities for fabricating structures with complex geometries. However, application of this technique to fabricate internal hollow channels in viscoelastic material has not been yet explored to a great extent. In this article, we present the theoretical modeling/background of a proposed manufacturing paradigm through which hollow channels can be conveniently fabricated inside a gel structure. We propose that a tip connected to a robotic arm can be moved in X-, Y-, and Z-axis as per the desired design. The tip can be moved by a magnet or mechanical force. If the tip is further trailed with porous tube and moved inside the viscoelastic material, corresponding internal channels can be fabricated. To achieve this, however, force modeling to understand the forces that will be required to move the tip inside viscoelastic material should be known and understood. Therefore, in our first attempt, we developed the computational force modeling of the tip movement inside gels with different viscoelastic properties to create the channels.
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