The tissue engineering approach for repairing osteochondral (OC) defects involves the fabrication of a biological tissue scaffold that mimics the physiological properties of natural OC tissue (e.g., the gradient transition between the cartilage surface and the subchondral bone). The OC tissue scaffolds described in many research studies exhibit a discrete gradient (e.g., a biphasic or tri/multiphasic structure) or a continuous gradient to mimic OC tissue attributes such as biochemical composition, structure, and mechanical properties. One advantage of a continuous gradient scaffold over biphasic or tri/multiphasic tissue scaffolds is that it more closely mimics natural OC tissue since there is no distinct interface between each layer. Although research studies to this point have yielded good results related to OC regeneration with tissue scaffolds, differences between engineered scaffolds and natural OC tissue remain; due to these differences, current clinical therapies to repair OC defects with engineered scaffolds have not been successful. This paper provides an overview of both discrete and continuous gradient OC tissue scaffolds in terms of cell type, scaffold material, microscale structure, mechanical properties, fabrication methods, and scaffold stimuli. Fabrication of gradient scaffolds with three-dimensional (3D) printing is given special emphasis due to its ability to accurately control scaffold pore geometry. Moreover, the application of computational modeling in OC tissue engineering is considered; for example, efforts to optimize the scaffold structure, mechanical properties, and physical stimuli generated within the scaffold–bioreactor system to predict tissue regeneration are considered. Finally, challenges associated with the repair of OC defects and recommendations for future directions in OC tissue regeneration are proposed.

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

Hydroxyapatite (HA, Ca₁₀(PO₄)₆(OH)₂) constitutes the major inorganic hard tissues, such as bone and teeth. Various substitutions in the apatite structure have been developed for a wide range of biomedical applications, such as the bone repair and tissue regenerations; bioactive and/or antibacterial coating for medical devices, biomarkers or carriers in drug/gene delivery systems, and the biomagnetic agents for cancer treatment. In this review, the recent development on substituted HA was overviewed from the aspects of enhanced osteoconductivity and multifunctionality of mono- and multi-cationic and anionic substitutions.