For three-dimensional bio-printed cell-laden hydrogel tissue constructs, the well-designed internal porous geometry is tailored to obtain the desired structural and cellular properties. However, significant differences often exist between the designed and as-printed scaffolds because of the inherent characteristics of hydrogels and cells. In this study, an iterative feedback bio-printing (IFBP) approach based on optical coherence tomography (OCT) for the fabrication of cell-laden hydrogel scaffolds with optimal geometrical fidelity and cellular controllability was proposed. A custom-made swept-source OCT (SS-OCT) system was applied to characterize the printed scaffolds quantitatively. Based on the obtained empirical linear formula from the first experimental feedback loop, we defined the most appropriate design constraints and optimized the printing process to improve the geometrical fidelity. The effectiveness of IFBP was verified from the second run using gelatin/alginate hydrogel scaffolds laden with C3A cells. The mismatch of the morphological parameters greatly decreased from 40% to within 7%, which significantly optimized the cell viability, proliferation, and morphology, as well as the representative expression of hepatocyte markers, includingCYP3A4and albumin, of the printed cell-laden hydrogel scaffolds. The demonstrated protocol paves the way for the mass fabrication of cell-laden hydrogel scaffolds, engineered tissues, and scaled-up applications of the 3D bio-printing technique.

Reconstructing and quantitatively assessing the internal architecture of opaque three-dimensional (3D) bioprinted hydrogel scaffolds is difficult but vital to the improvement of 3D bioprinting techniques and to the fabrication of functional engineered tissues. In this study, swept-source optical coherence tomography was applied to acquire high-resolution images of hydrogel scaffolds. Novel 3D gelatin/alginate hydrogel scaffolds with six different representative architectures were fabricated using our 3D bioprinting system. Both the scaffold material networks and the interconnected flow channel networks were reconstructed through volume rendering and binarisation processing to provide a 3D volumetric view. An image analysis algorithm was developed based on the automatic selection of the spatially-isolated region-of–interest. Via this algorithm, the spatially-resolved morphological parameters including pore size, pore shape, strut size, surface area, porosity, and interconnectivity were quantified precisely. Fabrication defects and differences between the designed and as-produced scaffolds were clearly identified in both 2D and 3D; the locations and dimensions of each of the fabrication defects were also defined. It concludes that this method will be a key tool for non-destructive and quantitative characterization, design optimisation and fabrication refinement of 3D bioprinted hydrogel scaffolds. Furthermore, this method enables investigation into the quantitative relationship between scaffold structure and biological outcome.

From the perspective of resolution, radiation damage, price–performance ratio, and other aggregate indicators, optical imaging technology featuring high spatiotemporal resolution, high information capacity, and nonionizing properties have garnered significant research attention in the fields of biomedical and material science.^1,2 Currently, high-resolution imaging technologies, such as fluorescence microscopy (FM),^{3 – 5} confocal microscopy (CM),^6,7 two-photon microcopy (TPM),^{8 – 11} optical coherence tomography (OCT),^12,13 and full-field optical coherence tomography (FF-OCT),^14,15 have been widely adopted. Each technology has its distinct features. Combining fluorescence probe and optical microscopy, FM can perform micron-scale high-resolution imaging of tissues, organoids, cells, and other samples. However, FM cannot provide depth information and exhibits phototoxicity to tissues.^16,17 Based on single-photon or two-photon excitation, CM and TPM render high imaging resolution and images with good contrast, but their imaging depth is mostly limited to a few hundred $\mu \mathrm{m}$.⁹ Based on the principle of low coherent interference, OCT can provide histology-like images of a sample noninvasively; the transverse resolution and axial resolution of OCT can be controlled, respectively, which can reach $10\mu \mathrm{m}$ or even $1\mu \mathrm{m}$; OCT imaging depth can reach 1 mm or higher, according to the scattering characteristics of samples, whereas the system cost is considerably lower than that of TPI. Therefore, OCT has attracted increased attention since it was first proposed in 1991.^12,13,18

3D cell culture models replicating the complexity of cell–cell interactions and biomimetic extracellular matrix (ECM) are novel approaches for studying liver cancer, including in vitro drug screening or disease mechanism investigation. Although there have been advancements in the production of 3D liver cancer models to serve as drug screening platforms, recreating the structural architecture and tumor-scale microenvironment of native liver tumors remains a challenge. Here, using the dot extrusion printing (DEP) technology reported in our previous work, we fabricated an endothelialized liver lobule-like construct by printing hepatocyte-laden methacryloyl gelatin (GelMA) hydrogel microbeads and HUVEC-laden gelatin microbeads. DEP technology enables hydrogel microbeads to be produced with precise positioning and adjustable scale, facilitating the construction of liver lobule-like structures. The vascular network was achieved by sacrificing the gelatin microbeads at 37 °C to allow HUVEC proliferation on the surface of the hepatocyte layer. Finally, we used the endothelialized liver lobule-like constructs for anti-cancer drug (Sorafenib) screening, and stronger drug resistance results were obtained when compared to either mono-cultured constructs or hepatocyte spheroids alone. The 3D liver cancer models presented here successfully recreate liver lobule-like morphology, and may have the potential to serve as a liver tumor-scale drug screening platform.

Three-dimensional (3D) bioprinting has emerged as a highly promising technology within the realms of tissue engineering and regenerative medicine. The assessment of printability is essential for ensuring the quality of bio-printed constructs and the functionality of the resultant tissues. Polymer materials, extensively utilized as bioink materials in extrusion-based bioprinting, have garnered significant attention from researchers due to the critical need for evaluating and optimizing their printability. Machine learning, a powerful data-driven technology, has attracted increasing attention in the evaluation and optimization of 3D bioprinting printability in recent years. This review provides an overview of the application of machine learning in the printability research of polymers for 3D bioprinting, encompassing the analysis of factors influencing printability (such as material and printing parameters), the development of predictive models, and the formulation of optimization strategies. Additionally, the review briefly explores the utilization of machine learning in predicting cell viability, evaluates the advanced nature and developmental potential of machine learning in 3D bioprinting, and examines the current challenges and future trends.