Patient-derived tumor organoids (PDTOs) are vital for precision oncology, but standard drug screening methods, including adenosine triphosphate (ATP) assays, are destructive and prevent longitudinal monitoring. Optical coherence tomography (OCT) offers non-destructive 3D imaging, capturing morphological features and tissue attenuation characteristics via the optical attenuation coefficient (OAC). Here, we propose a non-destructive evaluation framework for tumor organoids that fuses OAC and multi-dimensional morphological features. Using intrahepatic cholangiocarcinoma (iCCA) PDTOs treated with icaritin, we found that OAC exhibited a significant dose-dependent increase (up to 32.8% at 80 μM compared to control), accompanied by a morphological transition of cystic organoids into solid phenotypes. By integrating these features via K-means++ clustering and principal component analysis, we constructed a relative growth score. This fusion score correlated strongly with the ATP gold standard (Pearson correlation coefficient r = 0.938), outperforming a morphology-only model (r = 0.906). Furthermore, independent experiments with first-line chemotherapeutics (e.g., 5-Fluorouracil, Gemcitabine) and combinatorial regimens indicated the model's potential generalizability (r = 0.887). This method overcomes the limitations of destructive, single-metric evaluations, providing a quantitative and non-destructive platform for high-throughput drug screening and personalized treatment decision-making.

Patient-derived organoids (PDOs) hold transformative potential for personalized medicine by recapitulating patient-specific drug responses. While Optical Coherence Tomography (OCT) is ideal for monitoring these responses, its translation into high-throughput screening (HTS) is hindered by a segmentation accuracy-throughput bottleneck. Existing solutions fail to meet clinical demands: accurate 3D approaches are prohibitively slow, while emerging foundation models lack sensitivity to minute, low-contrast OCT targets. Conversely, fast 2D models suffer from background noise and unstable performance across varying scales. To bridge this gap, we propose DICE-2DSeg, a physics-informed, graph-enhanced framework. By synergizing OCT-inspired intra-slice coherent enhancement with graph-based inter-slice context aggregation, our method ensures robust quantification. Validated on 93 volumes across diverse cancer types and drugs, DICE-2DSeg demonstrates exceptional robustness. Specifically, our high-throughput variant achieves a 14-fold speedup over nnUNet3D while retaining 93.65% of its accuracy. Crucially, it exhibits superior multi-scale consistency, establishing a new state-of-the-art for challenging drug-responsive remnants (0-100 μm) while maintaining high fidelity for massive clusters (> 100 μm). By resolving the conflict between precision, scalability, and scale-invariance, DICE-2DSeg provides a technical enabling step for automated, large-scale PDO drug screening.

The repair of articular cartilage damage is a major challenge in the biomedical field. Silk fibroin hydrogels have garnered considerable attention in cartilage repair due to the excellent biocompatibility. However, a critical challenge remains in decoupling their elastic modulus and stress relaxation rate-two core viscoelastic properties that jointly regulate cell fate-thus limiting their tailored application in cartilage regeneration. In this work, we achieved precise control over the elastic modulus and stress relaxation rate of silk fibroin hydrogels for the first time by modulating the self-assembly of silk fibroin. We elucidated the mechanisms by which self-assembly process impacts the viscoelastic properties of the hydrogels and successfully established a silk fibroin-based hydrogel system with independently tunable stress relaxation rates and elastic moduli. The effects of silk fibroin viscoelasticity on BMSC differentiation and cartilage regeneration were evaluated in vitro and in vivo. The synergistic combination of a low elastic modulus (E ∼ 6.5 kPa) and fast stress relaxation (τ1/2-12 s) significantly upregulated the expression of cartilage-related genes, including SOX9 and COL2A1, and enhanced the secretion of glycosaminoglycans (GAGs). The newly formed tissue exhibited a smooth surface and tight integration with the surrounding cartilage tissue. Histological analysis revealed a high degree of structural similarity to native cartilage. This study provides a robust theoretical foundation for the development of novel cartilage repair materials and holds the potential to advance the field of tissue engineering and regenerative medicine.

Multidrug resistance remains a formidable barrier in cancer therapy, frequently driven by elevated cysteine levels within the tumor microenvironment. To counter this challenge, CV-1, a cysteine-responsive near-infrared (NIR) photosensitizer, was engineered for dual functionality-enabling both photodynamic therapy (PDT) activation and wash-free fluorescence imaging upon irradiation with 670 nm NIR light. CV-1 remains in an inert, non-fluorescent state under normal physiological conditions but becomes selectively activated in cysteine-rich cancer cells, thereby addressing the poor specificity and systemic toxicity commonly associated with traditional PDT agents. Upon interaction with cysteine, CV-1 exhibits a 15-fold fluorescence enhancement at 675 nm, a 3.7-fold increase in singlet oxygen generation, and significant elevation in reactive oxygen species (ROS) production, facilitating precise imaging and effective PDT in resistant tumors. In vitro assays confirmed CV-1's minimal cytotoxicity in healthy cells, along with its capacity to detect and visualize both endogenous and exogenous cysteine in live-cell environments. Importantly, selective activation in cysteine-enriched conditions effectively triggered apoptosis in multidrug-resistant PANC-1 cells under light irradiation. Flow cytometric analysis using Annexin V-FITC/PI dual staining revealed that combination treatment of CV-1 and chemotherapy drugs under white light irradiation induced a high apoptosis rate of 76.1 %. Collectively, these findings underscore the potential of CV-1 as a robust theranostic platform for integrated cancer diagnosis and treatment, offering a promising strategy to overcome drug resistance in aggressive malignancies.

The osteochondral interface-characterized by a steep gradient in both composition and mechanical properties-remains one of the most challenging anatomical sites to regenerate. Reconstructing this spatially complex heterogeneity continues to confound conventional osteochondral grafts. Although multilayer scaffolds are widely adopted, interfacial delamination frequently compromises repair outcomes. Here, we report a self-healing, physically crosslinked silk fibroin-based 3D-printing ink that incorporates gelatin and nano-hydroxyapatite for the fabrication of bilayer scaffolds with robust interfacial bonding. By tuning ultrasonication time of silk fibroin and gelatin content, the ink exhibits exceptional printability and cytocompatibility, enabling >90% post-printing cell viability. Leveraging a dual-nozzle alternating-print strategy, we generated bilayer constructs that display a stable interface and layer-specific mechanical heterogeneity. Both upper- and lower-layer inks possess good self-healing capacity, eliminating delamination and yielding a monolithic scaffold. Functional analyses revealed significant upregulation of the chondrogenic marker type II collagen in the upper layer and the osteogenic marker RUNX2 in the lower layer, achieving bidirectional lineage instruction required for osteochondral regeneration. This silk/gelatin-based, physically crosslinked, integrative bilayer scaffold offers a promising therapeutic platform for osteochondral defect repair.

Multicellular hepatic spheroids that recapitulate biomimetic cellular assembly and enhanced cellular interaction have become useful 3D culture models for liver tissue engineering and hepatotoxic drug screening. Despite progress, current strategies for generating heterogeneous spheroids with multiple cell types mostly depend on cell random aggregation, lacking a well-defined architecture that mimics natural liver tissue structure. Here, a microarray chip with a triangular chamber arrangement is developed to the fabrication of heterogeneous spheroids comprising hepatocytes, stellate, and endothelial cells in a multilayer organization. The triple cell co-culture spheroids formed by in sequence seeding above three types of cells, yielding structures with a hepatic plate-like core orderly coated by stellate and endothelial shells. These structured spheroids exhibit an enhanced structural integrity and liver functions such as albumin, MRP2, and the CYP3A4 and CYP1A2 enzyme expression levels compared to randomly distributed spheroids. Screening of hepatotoxic drugs by the hepatic spheroid microarray show increased sensitivity compared to 2D cultures. This approach represents a significant advancement toward control over cell spatial distribution in spheroids, and offers a valuable structured spheroid model with multilayer cellular organization for regenerative medicine and drug hepatotoxic evaluation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive solid tumor, closely associated with its unique tumor microenvironment (TME), which is characterized by a dense desmoplastic stroma. Abundant stromal cells, primarily fibroblasts, constitute the majority of cells in the tumor mass and exhibit pronounced spatial heterogeneity. Importantly, the spatial distribution of tumors and fibroblasts is vital for shaping the TME and critically influencing therapeutic responses. Here, we present a facile microarray chip for generating architecturally defined 3D PDAC heterospheroids. This platform enables us to mimic the dynamic interactions between tumor and stromal cells and to investigate how spatial organization influences stroma heterogeneity, tumor invasion and chemoresistance. The chip incorporates square concave microstructure array allowing controllable and reproducible production of uniform-sized spheroids. By simply altering the cell seeding sequence, we successfully constructed heterospheroids with distinct spatial distributions of cancer cells and fibroblasts. We further demonstrated that these organizational patterns modulate tumor-stroma crosstalk and ultimately regulate tumor invasive behavior. Furthermore, the heterospheroids with defined patterns exhibited distinct drug responses, and the potential for combination therapy evaluation was also verified. Beyond providing a robust platform for engineering heterospheroids with controllable tumor-stroma architectures, this system offers a robust 3D co-cultured model for advancing cancer research and drug screening.

The natural extracellular matrix (ECM) exhibits remarkable viscoelasticity and stress relaxation. Constructing viscoelastic scaffolds that can precisely control the stress relaxation rate and possess good biocompatibility is a key challenge in the design of tissue engineering scaffolds. Understanding the factors influencing the viscoelasticity of scaffolds and their mechanisms, as well as implementing comprehensive regulatory strategies based on this understanding, are effective methods for precisely controlling the stress relaxation rate. Current research on viscoelastic scaffolds mainly focuses on the regulation of bulk hydrogel viscoelasticity, while the impact of 3D printing parameters on stress relaxation time remains underexplored. In this study, we controlled the structure and morphology of silk fibroin to obtain a crystalline silk fibroin fiber (SL) solution, which was then mixed with gelatin solution to achieve high-precision printing of low-concentration (<2%) silk fibroin. Based on this, we explored the effects of printing angle, fiber diameter, and porosity on the stress relaxation rate and elastic modulus of the scaffold. Specifically, as porosity increases, the relaxation rate tends to rise, while the elastic modulus decreases. Conversely, as the printing angle and fiber diameter increase, the relaxation rate significantly decreases, and the elastic modulus correspondingly increases. We verified these effects using alginate-based bioink, demonstrating the universality of the influence of printing parameters on scaffold viscoelasticity. Additionally, we constructed scaffolds with similar elastic moduli but different stress relaxation rates and investigated their effects on cell growth, thereby confirming the good biocompatibility of viscoelastic scaffolds. This study not only provides a theoretical basis for precisely controlling the stress relaxation rate of 3D-printed viscoelastic scaffolds but also offers new insights for the design and optimization of tissue engineering scaffolds.

Traditional full-field optical coherence tomography (FF-OCT) systems face limitations in imaging efficiency and data redundancy, particularly when applied to complex biological samples. To address these issues, we developed an automated FF-OCT system that uses a variable resolution z-scanning strategy to dynamically adjust the voxel resolution of acquired images according to sample structures. The system was validated using 3D HepaRG cell cultures embedded in micro-hydrogels. To optimize imaging efficiency and minimize data redundancy, an in-air voxel resolution of 0.7×0.7×5µm3 was applied to the region of interest (ROI) for detailed sample analysis, while a lower resolution of 1.4×1.4×10µm3 was used in non-ROI areas. Compared to traditional equidistant acquisition methods, the variable resolution strategy reduced imaging time by over 20% and data storage requirements by more than 35%, with deviations in morphological parameters, including volume and surface area, kept below 1%. Robustness tests across multiple cell culture batches confirmed the system's reliability in accurately capturing complex biological structures. This study demonstrates a significant advancement in FF-OCT technology, providing a practical, high-efficiency, and high-precision solution for non-invasive imaging of complex biological samples.

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.

In three-dimensional (3D) bioprinting, the internal channel network is vital for nutrient and oxygen transport, crucial for cell survival and tissue construction. However, bioinks' poor mechanical properties hinder precise control over these networks. Advancements in 3D printing strategies, structure characterization, and deformation monitoring can improve hydrogel scaffolds with interconnected channels. Using label-free, non-invasive, in-situ optical coherence tomography (OCT) imaging, we monitored the dynamic deformation of 3D bioprinted hydrogel scaffolds. We validated sacrificial materials' role in enhancing internal channels and introduced deformation characteristics, lateral pore ratio and pore-specific surface area as new parameters. Results from cell-laden hydrogels show that 3D bioprinting with sacrificial materials achieves high fidelity, minimizing collapse, inter-filament fusion, and enhancing lateral porosity. Furthermore, these promote cell proliferation and cell viability.

Significance:Hepatocyte spheroids (HCSs) are three-dimensional (3D) in vitro models that exhibit a multilayered structure with site-dependent cell viability. The non-invasive identification of HCS structure and viability variation is essential in fully exploiting the potential of HCS as a model for liver disease research. Aim:We aim to achieve long-term, non-invasive monitoring and quantification of HCS cell viability based on dynamic optical coherence tomography (D-OCT) and enhance visualization of HCS internal activity with D-OCT pseudo-color images. Approach:We employed D-OCT based on power spectrum analysis with an appropriate optical coherence tomography time-series image acquisition rate to obtain the motion frequency distribution of cells within HCS, thus distinguishing and segmenting the viable and necrotic cell layers based on the average frequency of cellular activity, and quantify the tissue activity using the pixel ratio of the segmented viable region to the total spheroid region. Meanwhile, we used the hue saturation value color mapping method to enable enhanced visualization and high-precision segmentation of viable and necrotic cell layers in HCS. Results:The feasibility of the D-OCT method was verified experimentally with three sets of HCS samples (HCS-2000, HCS-5000, and HCS-10000) by comparison with a confocal laser scanning microscope. The cells in C3A-HCS were found to be active mainly in the range of 8 to 13 Hz by D-OCT detection. 3D D-OCT pseudo-color images of HCS with a maximum diameter of 450    μ m were displayed, and the 3D structures of necrotic and viable cell layers were identified by mask segmentation based on the average cell activity frequency threshold (10.5 Hz). The longitudinal necrotic process of three sets of HCS samples with differing inoculated cell numbers was monitored and quantified over 29 days. Conclusions:The employed D-OCT method can be used to quantitatively evaluate the site-dependent cell viability in HCS and possesses the potential for long-term, non-invasive monitoring and quantification of HCS viability.

Objective Tumor organoids,as novel in vitro tumor models,hold significant value in tumor biology research and personalized drug sensitivity assessment.However,existing methods relying on manual seeding and destructive endpoint testing are limited by the lack of dynamic monitoring capabilities and the requirement for high sample homogeneity.This study aims to develop a non-destructive,dynamic analysis framework for tumor organoids based on 3D optical coherence tomography(OCT)and deep learning,enabling precise segmentation,morphological characterization,and growth analysis of organoids to assess drug responses efficiently. Methods We presented a label-free OCT-based framework that includes deep learning-driven segmentation,3D morphometric quantification of individual organoids,and growth rate modeling of organoid clusters.To tackle 3D discontinuities in organoid segmentation,we introduced a novel parallel encoder architecture,ParaSAM2CNN,which integrates ResNet's deep feature extraction with SAM2's multiscale feature capture,enabling automated and precise segmentation(Dice coefficient:0.8026).An adaptive surface roughness quantification algorithm was developed to enable longitudinal,high-throughput,multidimensional morphological characterization of organoids.Unsupervised clustering was applied to categorize organoid phenotypes,while principal component analysis(PCA)was employed to elucidate correlations among morphological parameters,growth dynamics,and drug response.A growth level model for organoid clusters was established and validated against traditional destructive ATP-based assays,showing high consistency(90.45％). Results and Discussions The proposed framework demonstrates significant advantages in the non-destructive analysis of tumor organoids and drug response assessment.The ParaSAM2CNN model achieves superior segmentation performance compared to other state-of-the-art models,with improved precision and Jaccard index.The adaptive surface roughness algorithm provides detailed morphological characterization,capturing changes in organoid structure under drug treatment,such as the transition from cystic to solid phenotypes.The growth level model shows a high correlation with ATP test results,confirming its reliability in assessing organoid growth and drug sensitivity.This framework not only provides a non-invasive alternative to traditional endpoint testing but also offers a transformative potential for drug screening and personalized therapy optimization based on patient-derived tumor organoids. Conclusions This study presents a significant advancement in the analysis and application of tumor organoids for cancer research and treatment.By integrating OCT imaging with deep learning and machine learning techniques,we have developed a comprehensive and non-destructive evaluation framework that accurately assesses organoid growth and drug responses.This method has the potential to revolutionize traditional drug screening and sensitivity testing methods,providing a new technological platform for cancer research and personalized medicine.The high consistency with ATP testing highlights the model's potential as a reliable and non-invasive tool for cancer treatment.

A facile embedded dot bioprinting system for bioengineering desmoplastic PDAC spheroids with scalable, flexible and robust performance, or multi-type spheroid patterns for advanced drug therapy or disease mechanism exploration, is introduced.

To address the challenges associated with achieving high-fidelity printing of complex 3D bionic models, this paper proposes a method for spatially resolved defect characterization and fidelity assessment. This approach is based on 3D printer-associated optical coherence tomography (3D P-OCT) and GCode information. This method generates a defect characterization map by comparing and analyzing the target model map from GCode information and the reconstructed model map from 3D P-OCT. The defect characterization map enables the detection of defects such as material accumulation, filament breakage and under-extrusion within the print path, as well as stringing outside the print path. The defect characterization map is also used for defect visualization, fidelity assessment and filament breakage repair during secondary printing. Finally, the proposed method is validated on different bionic models, printing paths and materials. The fidelity of the multilayer HAP scaffold with gradient spacing increased from 0.8398 to 0.9048 after the repair of filament breakage defects. At the same time, the over-extrusion defects on the nostril and along the high-curvature contours of the nose model were effectively detected. In addition, the finite element analysis results verified that the 60-degree filling model is superior to the 90-degree filling model in terms of mechanical strength, which is consistent with the defect detection results. The results confirm that the proposed method based on 3D P-OCT and GCode can achieve spatially resolved defect characterization and fidelity assessment in situ, facilitating defect visualization and filament breakage repair. Ultimately, this enables high-fidelity printing, encompassing both shape and function.

Objective Three-dimensional(3D)tumor organoids,serving as in vitro models that replicate the critical structural and functional features of organs and tumor tissues,have demonstrated their unique value in disease modeling,personalized medicine,and drug screening.Patient-derived organoids(PDOs)not only recapitulate the morphological characteristics and physiological functions of their original tissues but also maintain the genetic and heterogeneity of tumors,rendering them invaluable resources for cancer research and treatment.However,current methods for analyzing organoid growth and drug effects have limitations,particularly in the absence of 3D high-throughput and label-free monitoring tools,hampering the more effective assessment of organoid growth and drug actions.To address this challenge,this study is dedicated to developing a comprehensive evaluation method based on optical coherence tomography(OCT)and machine learning algorithms.The aim is to establish a novel,non-invasive,label-free tool for the morphological characterization of organoids,enabling longitudinal evaluation of their responses to drug treatments.This approach holds significant potential for the application of PDOs in personalized cancer therapy,particularly for intrahepatic cholangiocarcinoma(iCCA),for which treatment options are limited. Methods In this study,we propose a method that combines OCT imaging with machine learning to perform longitudinal,accurate,label-free,and parallel morphological characterization of a large number of individual organoids within organoid clusters.Through 3D OCT imaging and organoid segmentation technology,we achieved 3D imaging and morphological analysis of individual organoids,including parameters such as organoid volume,organoid surface area,and organoid cavity volume.Subsequently,based on undersampling,we conducted a cluster analysis on multiple organoids within the organoid clusters to obtain statistical information on multi-dimensional morphological parameters for different categories.Feature selection and principal component analysis(PCA)were then applied to construct a comprehensive evaluation scoring function that combines the factor scores of each principal component and weights according to their variance contribution rates.Furthermore,we characterized the relative growth value of organoid clusters by calculating the difference in the comprehensive evaluation scores of the growth levels between two time points.Alternatively,the growth rate of the organoid clusters was represented by the slope of linear fitting based on the comprehensive evaluation scores from multiple time points.Ultimately,we validated the effectiveness of the comprehensive evaluation model of the growth levels based on the organoid clusters and PCA using adenosine triphosphate(ATP)testing results. Results and Discussions Our study results highlight the significant advantages of OCT imaging and machine learning in characterizing organoid growth and drug responses.A notable correlation is observed between organoid morphological changes and drug treatments,such as the transition of cystic organoids to solid organoids under the influence of medication(Fig.3).The comprehensive evaluation model that we constructed shows an 82.9％consistency with traditional ATP biochemistry testing,which is a widely recognized indicator of cellular activity and proliferation(Table 5).More importantly,the correlation between the relative growth values derived from our comprehensive evaluation model and ATP measurements reaches an impressive 90.4％.This high degree of consistency confirms that our model can serve as a reliable proxy for assessing organoid growth and drug sensitivity.Additionally,the study results underscore the potential of our method to reveal morphological changes in organoids,which may be significant indicators of drug response and may provide new insights into the complexity of tumor-drug interactions. Conclusions This study marks significant progress in the field of organoid research and its implications for cancer treatment.By integrating OCT with machine learning,we have developed a robust and comprehensive evaluation model that is capable of accurately assessing organoid growth levels and responses to drugs.This method stands poised to revolutionize traditional approaches to drug efficacy screening and sensitivity testing,particularly for PDOs.The high consistency observed between our evaluation model and traditional ATP testing underscores its potential as a reliable and non-invasive tool in cancer research.As we transition into the era of personalized medicine,the precise measurement and prediction of individual organoid drug responses are becoming increasingly crucial.The methodology outlined in this study not only reveals the morphological changes of organoids under the influence of drugs but also lays the groundwork for a new technological platform for cancer drug screening and clinical drug sensitivity testing based on PDOs.Its aim is twofold:to deepen our understanding of tumor biology and to advance the development of more precise and effective cancer treatment strategies.

Since hepatic cancer incidence and mortality continue to grow worldwide, it is necessary to develop the biomimetic tumor models for drug development and tumor therapeutics. Cellular spheroids as an excellent simple 3D model can bridge the gap between 2D cell culture and live tissue. In this study, we proposed a biological methacrylated gelatin (GelMA) hydrogel-based microplatform for the massive generation of hepatocellular spheroids and downstream investigation of drug resistance. Micropatterned GelMA hydrogel microwell chip (GHM-chip) with tunable array was easily achieved in standard 24-culture well plates through the micro-molding fabrication strategy. The fabricated GHM-chip induced multicellular self-assembly behavior within the defined topography and further formed spheroidal structure. By regulating cell seeding density and designing microwell size, uniform hepatic cancer spheroids with tunable diameters were obtained in a simplicity, stability and controllable manner. In addition, the screening chemotherapy study of anti-cancer drug was completed through non-destructive recovery of spheroids from the GHM-chip. Beyond that, the recovered functional spheroids have potential application value in various biomedical fields such as tumor biology, pharmacology, and tissue microengineering. Finally, the proposed GHM-chip incorporated into standard cell culture plates with easy to manufacture and operate properties, may be an efficient culture microplatform for cancer research applications.

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Extrusion-based bioprinting is a widely used approach to construct artificial organs or tissues in the medical fields due to its easy operation and good ability to combine multimaterial. Nevertheless, the current technology is limited to some printing errors when combining multi-material printing, including mismatch between printing filaments of different materials and error deposited materials (e.g., under-extrusion and overextrusion). These errors will affect the function of the printed structure (e.g., mechanical and biological properties), and the traditional manual correction methods are inefficient in time and material, so an automatic procedure is needed to improve multimaterial printing accuracy and efficiency. However, to the best of our knowledge, very few automated procedure can achieve the registration between printing filaments of different materials. Herein, we utilized optical coherence tomography (OCT) to monitor printing process and presented a multi-material static model and a time-related control model in extrusion-based multi-material bioprinting. Specifically, the multi-material static model revealed the relationship between printed filament metrics (filament size and layer thickness) and printing parameters (printing speeds or pressures) with different materials, which enables the registration of printing filaments by rapid selection of printing parameters for the materials, while time-related control model could correct control parameters of nozzles to reduce the material deposition error at connection point between nozzles in a short time. According to the experimental results of singlelayer scaffold and multi-layer scaffold, material deposition error is eliminated, and the same layer thickness between different materials of the same layer is achieved, which proves the accuracy and practicability of these models. The proposed models could achieve improved precision of printed structure and printing efficiency.

Extrusion-based three-dimensional (3D) bioprinting is one of the most common methods used for tissue fabrication and is the most widely used additive manufacturing technique in all industries. In extrusion-based bioprinting, printing defects related to material deposition errors lead to a significant deviation from shape to function between the printed construct and design model. Using 3D extrusion-based bioprinter-associated optical coherence tomography (3D P-OCT), an in situ defect detection and feedback system was presented based on the accurate defect analysis and location, and a pre-built feedback mechanism. Using 3D P-OCT, multi-parameter quantification of the material deposition was carried out in real time, including the filament size, layer thickness, and layer fidelity. The material deposition errors under different paths were quantified and located specifically, including the start-stop points, straight-line path, and turnarounds. The pre-built feedback mechanism involving the control inputs, such as printing path, pressure, and velocity, provided the basis for in situ defect detection and real-time feedback control. In particular, the second printing repair can be performed after the broken filament defect is detected and located. After printing, fidelity can be quantitatively analyzed based on the point cloud registration between the 3D P-OCT result and the design model. In conclusion, 3D P-OCT enables in situ defect detection and feedback control, broken filament repair, and 3D fidelity analysis to achieve high-fidelity printing from shape to function.