Bioprinting enables the fabrication of complex, heterogeneous tissues through robotically-controlled placement of cells and biomaterials. It has been rapidly developing into a powerful and versatile tool for tissue engineering. Recent advances in bioprinting modalities and biofabrication strategies as well as new materials and chemistries have led to improved mimicry and development of physiologically relevant tissue architectures constituted with multiple cell types and heterogeneous spatial material properties. Machine learning (ML) has been applied to accelerate these processes. It is a new paradigm for bioprinting. In this review, we explore current trends in bioink formulation and how ML has been used to accelerate optimization and enable real-time error detection as well as to reduce the iterative steps necessary for bioink formulation. We examined how rheometric properties, including shear storage, loss moduli, viscosity, shear-thinning property of biomaterials affect the printability of a bioink. Furthermore, we scrutinized the interplays between yield shear stress and the printability of a bioink. Moreover, we systematically surveyed the application of ML in precision in situ surgical site bioprinting, closed-loop AI printing, and post-printing optimization.

Three-dimensional (3D) bioprinting enables the creation of tissue constructs with heterogeneous compositions and complex architectures. It was initially used for preparing scaffolds for bone tissue engineering. It has recently been adopted to create living tissues, such as cartilage, skin, and heart valve. To facilitate vascularization, hollow channels have been created in the hydrogels by 3D bioprinting. This review discusses the state of the art of the technology, along with a broad range of biomaterials used for 3D bioprinting. It provides an update on recent developments in bioprinting and its applications. 3D bioprinting has profound impacts on biomedical research and industry. It offers a new way to industrialize tissue biofabrication. It has great potential for regenerating tissues and organs to overcome the shortage of organ transplantation.

The side effects of radio- and chemo-therapy pose long-term challenges on a cancer patient’s health. It is, therefore, highly desirable to develop more effective therapies that can specifically target carcinoma cells without damaging normal and healthy cells. Tremendous efforts have been made in the past to develop targeted drug delivery systems for solid cancer treatment. In this study, a new aptamer, A10-3-J1, which recognizes the extracellular domain of the prostate specific membrane antigen (PSMA), was designed. A super paramagnetic iron oxide nanoparticle-aptamer-doxorubicin (SPIO-Apt-Dox) was fabricated and employed as a targeted drug delivery platform for cancer therapy. This DNA RNA hybridized aptamer antitumor agent was able to enhance the cytotoxicity of targeted cells while minimizing collateral damage to non-targeted cells. This SPIO-Apt-Dox nanoparticle has specificity to PSMA⁺ prostate cancer cells. Aptamer inhibited nonspecific uptake of membrane-permeable doxorubic to the non-target cells, leading to reduced untargeted cytotoxicity and endocytic uptake while enhancing targeted cytotoxicity and endocytic uptake. The experimental results indicate that the drug delivery platform can yield statistically significant effectiveness being more cytotoxic to the targeted cells as opposed to the non-targeted cells.

Success in the differentiating human embryonic stem cells (hESCs) into insulin-secreting β cells raises new hopes for diabetes treatment. In this work, we demonstrated the feasibility of developing islet organoids from hESCs within biomimetic 3D scaffolds. We showed that such a 3D microenvironment is critical to the generation of pancreatic endoderm and endocrine from hESCs. The organoids formed consisted of pancreatic α, β, δ, and pancreatic polypeptide (PP) cells. A high-level co-expression of PDX1, NKX6.1, and NGN3 in these cells suggests the characteristics of pancreatic β cells. More importantly, most insulin-secreting cells generated did not express glucagon, somatostatin, or PP. The expression of mature β cell marker genes such as Pdx1, Ngn3, Insulin, MafA, and Glut2 was detected in these 3D-induced cell clusters. A high-level expression of C-peptide confirmed the de novo endogenous insulin production in these 3D induced cells. Insulin-secretory granules, an indication of β cell maturity, were detected in these cells as well. Glucose challenging experiments suggested that these cells are sensitive to glucose levels due to their elevated maturity. Exposing the cells to a high concentration of glucose induced a sharp increase in insulin secretion.