Bioprinting is an emerging technique for the fabrication of three-dimensional (3D) cell-laden constructs. However, the progress for generating a 3D complex physiological microenvironment has been hampered by a lack of advanced cell-responsive bioinks that enable bioprinting with high structural fidelity, particularly in the case of extrusion-based bioprinting. Herein, we report a novel strategy to directly bioprint cell-laden constructs using bioinks made of gelatin methacryloyl (GelMA) physical gels (GPGs). Attributed to their shear-thinning and self-healing properties, the GPG bioinks could retain the shape and form integral structures after deposition, allowing for subsequent UV crosslinking for permanent stabilization. We showed the structural fidelity by bioprinting various 3D structures that are typically challenging to fabricate using conventional bioinks under extrusion modes. Moreover, the use of the GPG bioinks enabled direct bioprinting of highly porous and soft constructs at relatively low concentrations (down to 3%) of GelMA. We also demonstrated that the bioprinted constructs not only permitted cell survival but also enhanced cell proliferation as well as spreading at lower concentrations of the GPG bioinks. We believe our strategy of bioprinting will provide many opportunities in convenient fabrication of 3D cell-laden constructs for applications in tissue engineering, regenerative medicine, and pharmaceutical screening.
The development of a multimaterial extrusion bioprinting platform is reported. This platform is capable of depositing multiple coded bioinks in a continuous manner with fast and smooth switching among different reservoirs for rapid fabrication of complex constructs, through digitally controlled extrusion of bioinks from a single printhead consisting of bundled capillaries synergized with programmed movement of the motorized stage.
Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs – “4-way”, “7-way”, and “10-way” – each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS “physiome-on-a-chip” approaches in drug discovery.
Bioinks with shear-thinning/rapid solidification properties and strong mechanics are usually needed for the bioprinting of three-dimensional (3D) cell-laden constructs. As such, it remains challenging to generate soft constructs from bioinks at low concentrations that are favorable for cellular activities. Herein, we report a strategy to fabricate cell-laden constructs with tunable 3D microenvironments achieved by bioprinting of gelatin methacryloyl (GelMA)/alginate core/sheath microfibers, where the alginate sheath serves as a template to support and confine the GelMA pre-hydrogel in the core during the extrusion process, allowing for subsequent UV crosslinking. This novel strategy minimizes the bioprinting requirements for the core bioink, and facilitates the fabrication of cell-laden GelMA constructs at low concentrations. We first showed the capability of generating various alginate hollow microfibrous constructs using a coaxial nozzle setup, and verified the diffusibility and perfusability of the bioprinted hollow structures that are important for the tissue engineering applications. More importantly, the hollow alginate microfibers were then used as templates for generating cell-laden GelMA constructs with soft microenvironments, by using GelMA pre-hydrogel as the bioink for the core phase during bioprinting. As such, GelMA constructs at extremely low concentrations (<2.0%) could be extruded to effectively support cellular activities including proliferation and spreading for various cell types. We believe that our strategy is likely to provide broad opportunities in bioprinting of 3D constructs with cell-favorable microenvironments for applications in tissue engineering and pharmaceutical screening.
Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution. This new convolution operator can better capture the local neighborhood geometry of each point by specifying the (regular and dilated) ring-shaped structures and directions in the computation. It can adapt to the geometric variability and scalability at the signal processing level. We apply it to the developed hierarchical neural networks for object classification, part segmentation, and semantic segmentation in large-scale scenes. The extensive experiments and comparisons demonstrate that our approach outperforms the state-of-the-art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40, ShapeNetpart, S3DIS, and ScanNet).
Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT + BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D , and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.
Hydrogels with adhesive properties have potential for numerous biomedical applications. Here, the design of a novel, intrinsically adhesive hydrogel and its use in developing internal therapeutic bandages is reported. The design involves incorporation of “triple hydrogen bonding clusters” (THBCs) as side groups into the hydrogel matrix. The THBC through a unique “load sharing” effect and an increase in bond density results in strong adhesions of the hydrogel to a range of surfaces, including glass, plastic, wood, poly(tetrafluoroethylene) (PTFE), stainless steel, and biological tissues, even without any chemical reaction. Using the adhesive hydrogel, tissue‐adhesive bandages are developed for either targeted and sustained release of chemotherapeutic nanodrug for liver cancer treatment, or anchored delivery of pancreatic islets for a potential type 1 diabetes (T1D) cell replacement therapy. Stable adhesion of the bandage inside the body enables almost complete tumor suppression in an orthotopic liver cancer mouse model and ≈1 month diabetes correction in chemically induced diabetic mice.
Sparse representation has shown an attractive performance in a number of applications. However, the available sparse representation methods still suffer from some problems, and it is necessary to design more efficient methods. Particularly, to design a computationally inexpensive, easily solvable, and robust sparse representation method is a significant task. In this paper, we explore the issue of designing the simple, robust, and powerfully efficient sparse representation methods for image classification. The contributions of this paper are as follows. First, a novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results. More importantly, the proposed method outperforms the existing state-of-the-art sparse representation methods. Second, the proposed method is not only very computationally efficient but also has an intuitive and easily understandable idea. It exploits a simple algorithm to obtain a closed-form solution and discriminative representation of the test sample. Third, the feasibility, computational efficiency, and remarkable classification accuracy of the proposed l₂ regularization-based representation are comprehensively shown by extensive experiments and analysis. The code of the proposed method is available at http://www.yongxu.org/lunwen.html.
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